Science, Faculty of
DSpace
UBCV
Alghamdi, Dalia
Doctor of Philosophy - PhD
University of British Columbia
Specimens and data sharing through biobanking activities has become increasingly important in global biomedical research. These practices offer the potential to accelerate scientific discovery by enabling the reuse of biospecimens and associated data. However, the benefits of data sharing can only be realized when data are findable, accessible, interoperable, and reusable (FAIR). Biobanks remain fragmented—each operating with different standards, data formats, access policies, and query interfaces—making it difficult for researchers to locate and integrate specimen-derived data. This thesis explores the application of semantic web technologies (e.g., RDF, SPARQL, and ontologies) to improve the discovery and integration of biobank data, particularly omics-derived datasets. In Chapter 1, I provide an overview of the evolution and significance of biobanks in biomedical research, highlighting their transition from physical repositories to data-centric infrastructures, and discuss the persistent challenges researchers encounter in locating, accessing, and integrating fragmented and non-standardized specimen-derived data. In Chapter 2, I examine the current challenges in biobank data discovery and integration, including the lack of standardized contextual metadata, inconsistent identifiers, and insufficient support for automated data reuse. I propose semantic web approaches as a viable solution to these problems by enabling machine-readable, interoperable metadata and flexible, federated querying. In Chapter 3, I present the development of the Next-Generation Biobanking Ontology (NGBO), an open-source application ontology designed to represent omics contextual metadata. It provides persistent identifiers and semantically rich metadata to support both human understanding and computational reasoning. Chapter 4 describes a proof-of-concept implementation of a semantic web application that enables federated querying of biobank metadata across multiple SPARQL endpoints. Chapter 5 offers a structured review of how ontologies have been applied in biomedical text mining, with the aim of clarifying the strengths, limitations, and open challenges of ontology-based approaches in this field. The review was undertaken to identify opportunities for leveraging text-mined knowledge in biobank data ecosystems and to inform recommendations for future applications. Chapter 6 presents the discussion, conclusion, and future directions, reflecting on the key contributions of the thesis and outlining recommendations for advancing semantic web technologies in biobank informatics.
Introduction of omics metadata into biobanking ontology by Dalia Alghamdi B.Sc., King Abdulaziz University, 2013 M.Sc., University of Leicester, 2014 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Doctor of Philosophy in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Bioinformatics) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) November 2025 © Dalia Alghamdi, 2025 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Introduction of omics metadata into biobanking ontology submitted by Dalia Alghamdi in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Bioinformatics Examining Committee: Dr. William Hsiao, Clinical Associate Professor, Dept of Pathology & Laboratory Medicine, UBC Supervisor Dr. Mel Krajden, Professor, Dept of Pathology & Laboratory Medicine, UBC Supervisory Committee Member Dr. Rachel Pottinger, Professor, Dept of Computer Science, UBC Supervisory Committee Member Dr. Patrick Tang, Clinical Professor, Dept of Pathology & Laboratory Medicine, UBC University Examiner Dr. Steven Jones, Professor, Dept of Medical Genetics, UBC University Examiner Additional Supervisory Committee Members: Dr. Elodie Portales-Casamar, Clinical Associate Professor, Department of Pediatrics, Université de Montréal Supervisory Committee Member iii Abstract Specimens and data sharing through biobanking activities has become increasingly important in global biomedical research. These practices offer the potential to accelerate scientific discovery by enabling the reuse of biospecimens and associated data. However, the benefits of data sharing can only be realized when data are findable, accessible, interoperable, and reusable (FAIR). Biobanks remain fragmented—each operating with different standards, data formats, access policies, and query interfaces—making it difficult for researchers to locate and integrate specimen-derived data. This thesis explores the application of semantic web technologies (e.g., RDF, SPARQL, and ontologies) to improve the discovery and integration of biobank data, particularly omics-derived datasets. In Chapter 1, I provide an overview of the evolution and significance of biobanks in biomedical research, highlighting their transition from physical repositories to data-centric infrastructures, and discuss the persistent challenges researchers encounter in locating, accessing, and integrating fragmented and non-standardized specimen-derived data. In Chapter 2, I examine the current challenges in biobank data discovery and integration, including the lack of standardized contextual metadata, inconsistent identifiers, and insufficient support for automated data reuse. I propose semantic web approaches as a viable solution to these problems by enabling machine-readable, interoperable metadata and flexible, federated querying. In Chapter 3, I present the development of the Next-Generation Biobanking Ontology (NGBO), an open-source application ontology designed to represent omics contextual metadata. It provides persistent identifiers and semantically rich metadata to support both human iv understanding and computational reasoning. Chapter 4 describes a proof-of-concept implementation of a semantic web application that enables federated querying of biobank metadata across multiple SPARQL endpoints. Chapter 5 offers a structured review of how ontologies have been applied in biomedical text mining, with the aim of clarifying the strengths, limitations, and open challenges of ontology-based approaches in this field. The review was undertaken to identify opportunities for leveraging text-mined knowledge in biobank data ecosystems and to inform recommendations for future applications. Chapter 6 presents the discussion, conclusion, and future directions, reflecting on the key contributions of the thesis and outlining recommendations for advancing semantic web technologies in biobank informatics. v Lay Summary Biobanks store biological samples, such as blood or tissue, along with various types of associated data, including demographic, clinical, and molecular information. While these resources are valuable for biomedical research, the data is often unstructured and difficult for computers to process, limiting its usability. This thesis presents the Next Generation Biobanking Ontology (NGBO)—a structured vocabulary designed to describe omics-related biobank data. By applying semantic web technologies such as ontologies and RDF, NGBO improves how biobank data is organized, searched, and integrated across systems. I also develop a software tool that uses NGBO to enable federated querying with SPARQL, allowing researchers to retrieve specimen-related data stored in different locations without centralizing it. The system is designed to be flexible and adapt to evolving data sources with minimal effort. Additionally, I review how ontologies are applied in biomedical text mining to extract structured knowledge from scientific literature. These contributions enhance biobank data reuse and support scalable biomedical research. vi Preface In conjunction with my supervisor, William Hsiao, I was involved in the conceptualization and design of the methodology of all experiments presented in this thesis. I performed all of the formulation, data collection, data curation, coding, writing, and generation of figures and tables. Chapters 1 and 2: Chapters 1 and 2 have been written by me. Gap analysis, data acquisition, and data curation were performed by me under the supervision of William Hsiao, Damion Dooley, and Emma Griffiths. Chapter 3: A version of this material and associated figures and tables has been published in Bioinformatics Advances (https://academic.oup.com/bioinformaticsadvances/article/5/1/vbaf131/8224585). I drafted the manuscript, and all co-authors helped with the editing. The ontology design and planning were conducted primarily by William Hsiao and me. Data curation was primarily performed by me under the supervision of Damion Dooley and William Hsiao. Damion Dooley provided technical and implemental help at the ontology development stage. The ontology is publicly available at https://www.ebi.ac.uk/ols4/ontologies/ngbo Test files are available at https://github.com/Dalalghamdi/NGBO. vii The results of this chapter were presented as posters at The International Conference for Biomedical Ontologies (ICBO) (2019 and 2021). The abstract was published in: https://ceur-ws.org/Vol-2931/ICBO_2019_paper_56.pdf and https://ceur-ws.org/Vol-2180/paper-02.pdf. Chapter 4: A version of this material and its associated figures are being prepared as a manuscript for submission. I drafted the manuscript; all co-authors helped with the editing. The architecture of the federated query tool was designed and implemented by me. A student intern, Stephanie Quon, and I set up the SPARQL endpoints. The work was performed under the supervision of William Hsiao. This work is intended for submission as a journal manuscript by the end of the summer. Additionally, the results of this chapter were presented as an electronic poster at the Europe Biobank Week (EBW 2021). The abstract was published in: https://cdn-api.swapcard.com/public/files/928958c89f044768b2eff40bd1112559.pdf Chapter 5: A version of this material and its associated figures are being prepared as a manuscript for submission. I performed the comprehensive literature review, including the selection of studies, data extraction, analysis, and manuscript writing. My supervisor, William Hsiao, supervised the process and contributed to editing the manuscript. Statement on Use of Generative Artificial Intelligence: The example clinical note shown in Figure 5.2 was created with ChatGPT to protect patient privacy. It is based on real examples, but instead of including an actual note, I used Generative viii AI to generate similar text so that no real patient information would be shared. No other aspects of the research, drafting, or preparation of this thesis involved the use of generative AI. ix Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ......................................................................................................................... ix List of Tables ................................................................................................................................xv List of Figures ............................................................................................................................. xvi List of Abbreviations ............................................................................................................... xviii Acknowledgements .................................................................................................................... xxi Dedication .................................................................................................................................. xxii Chapter 1: Biobanks and big data sharing, an open-ended issue .............................................1 1.1 Author summary ............................................................................................................. 1 1.2 Introduction ..................................................................................................................... 3 1.2.1 Biobanks: definition and background ......................................................................... 6 1.2.2 From bio-specimens to big data .................................................................................. 7 1.2.2.1 The need for large scale data .............................................................................. 7 1.2.2.2 Shift towards data-centric biobanks .................................................................... 9 1.2.3 Challenges in modern data-centric biobanks .............................................................. 9 1.2.3.1 Data integration models and current challenges ............................................... 10 1.2.4 Challenges in data standardization and integration .................................................. 11 x 1.2.4.1 Global efforts to address biobanking data standardization ............................... 13 1.2.4.2 Global efforts to address omics data standardization ....................................... 15 1.3 Discussion ..................................................................................................................... 27 Chapter 2: Setting the context: motivation and semantic state-of-the-art .............................28 2.1 Author summary ........................................................................................................... 28 2.2 Introduction ................................................................................................................... 29 2.2.1 Preliminaries: key technologies (semantic web and a web of linked data) .............. 30 2.2.1.1 Resource description framework (RDF) ........................................................... 32 2.2.1.2 Web ontology language (OWL)........................................................................ 35 2.2.1.3 Description logic ............................................................................................... 37 2.2.1.4 SPARQL Protocol and RDF Query Language ................................................. 39 2.2.1.5 Ontologies ......................................................................................................... 40 2.2.1.6 Semantic reasoner ............................................................................................. 43 2.3 Current gaps .................................................................................................................. 44 2.3.1 Need for persistent identifiers and an application ontology ..................................... 44 2.3.2 Need for data granularity (What level of data can be accessed?) ............................. 53 2.3.3 Are we looking for data standards, minimum information requirements, or both? .. 56 2.4 Discussion ..................................................................................................................... 58 Chapter 3: Next Generation Biobanking Ontology (NGBO). ..................................................60 3.1 Author summary ........................................................................................................... 60 3.2 Introduction ................................................................................................................... 61 xi 3.3 Methods......................................................................................................................... 62 3.3.1 Defining NGBO scope .............................................................................................. 62 3.3.2 Technical development ............................................................................................. 70 3.4 Results ........................................................................................................................... 72 3.4.1 Ontologizing contextual data structure for wet bench analysis ................................ 73 3.4.2 Ontologizing contextual data structure for dry bench analysis. ................................ 73 3.4.3 Implementation of CAP Requirements in NGBO .................................................... 79 3.4.4 NGBO evaluation...................................................................................................... 86 3.4.4.1 NGBO evaluation by OBO Foundry................................................................. 86 3.4.4.2 NGBO evaluation by CQs and reasoners.......................................................... 87 3.4.5 NGBO use cases ....................................................................................................... 88 3.4.5.1 Enhancing data integration and discoverability in SHGP using NGBO........... 88 3.4.6 Application of NGBO for managing datasets generated in grant-funded projects in R2 ................................................................................................................................... 91 3.5 Additional features: ....................................................................................................... 92 3.5.1 Specimen tracking and traceability ........................................................................... 92 3.5.2 Data privacy and user access control ........................................................................ 95 3.6 Discussion ..................................................................................................................... 97 Chapter 4: Semantic web application for organizing and federated querying of biobank data......................................................................................................................................................100 4.1 Author summary ......................................................................................................... 100 xii 4.2 Introduction ................................................................................................................. 102 4.3 Related work ............................................................................................................... 105 4.4 Methods....................................................................................................................... 107 4.4.1 Data structure, data source description, and query language .................................. 107 4.4.2 Access restriction aspects of data federation using semantic web tools. ................ 110 4.4.3 Back-end development............................................................................................ 111 4.4.4 Graphical user interface (frontend development) ................................................... 112 4.5 Results ......................................................................................................................... 114 4.5.1 Excel sheet template for standardizing biobanking data attributes ......................... 114 4.5.2 Backend and front-end (graphical user interface) development ............................. 116 4.5.3 Evaluation ............................................................................................................... 121 4.6 Discussion ................................................................................................................... 122 Chapter 5: Leveraging ontologies for biomedical text mining ..............................................125 5.1 Author summary ......................................................................................................... 125 5.2 Introduction ................................................................................................................. 126 5.2.1 Preliminaries: Key Biomedical Text Mining Tasks and the Role of Ontologies ... 130 5.2.1.1 Natural language processing (NLP) ................................................................ 130 5.2.1.2 Text classification ........................................................................................... 134 5.2.1.3 Information extraction .................................................................................... 136 5.2.1.4 Named entity linking....................................................................................... 139 5.2.1.5 Embedding and pre-training ........................................................................... 140 xiii 5.2.1.6 Relation extraction and modeling ................................................................... 141 5.3 Methodology ............................................................................................................... 143 5.3.1 planning and scoping .............................................................................................. 143 5.3.2 Inclusion criteria ..................................................................................................... 144 5.4 Results ......................................................................................................................... 146 5.5 Discussion ................................................................................................................... 152 Chapter 6: Discussion, conclusion, and future directions ......................................................158 6.1 Outstanding Questions and Future Directions ............................................................ 158 6.1.1 Fine-grained healthcare data access control and cyber threats prevention using graph databases project (Approved grant) .................................................................................... 158 6.1.2 Leveraging ontologies for variant classification and decision support system (Approved grant). ................................................................................................................ 160 6.2 Discussion and Conclusion ......................................................................................... 162 Bibliography ...............................................................................................................................166 Appendices ..................................................................................................................................184 Appendix A Chapter 2 supplementary material ....................................................................... 184 A.1 An example of sample data form received from King Fahad Medical City (SHGP partner) ................................................................................................................................ 184 A.2 An example of sample data form received from Saudi Diagnostic Laboratory (SHGP partner) ................................................................................................................................ 185 Appendix B Chapter 3 supplementary material ....................................................................... 186 xiv B.1 Supplementary method - An example of description logic queries expressing competency questions. ........................................................................................................ 186 B.2 Supplementary method – Publications analyzed to identify contextual data elements for NGBO specifications..................................................................................................... 189 B.3 Supplementary method - An example of ontologised terms ................................... 191 Appendix C Chapter 4 supplementary material ....................................................................... 199 C.1 Supplementary method - Web application search questionnaire. ........................... 199 C.2 Supplementary method - SPARQL query example ................................................ 200 Appendix D Chapter 5 supplementary material ....................................................................... 201 D.1 Supplementary method - Summery on ontology-bases biomedical text mining related papers ................................................................................................................................. 201 xv List of Tables Table 1.1 MIxS Compliance and Implementation Metadata Standards ....................................... 22 Table 2.1 Examples of number of terms in domain ontologies .................................................... 48 Table 2.2 Examples of different interface terminologies used in clinical reports from diagnostic service providers ........................................................................................................................... 51 Table 3.1 NGBO specifications .................................................................................................... 65 Table 3.2 Comparison of NGBO use-case requirements with selected community standards and documentation guidelines ............................................................................................................. 69 Table 3.3 suggested information checklist for omics data generation assays ............................... 78 Table 3.4 Suggested minimum information checklist for Bioinformatics software. .................... 83 Table 3.5 Suggested minimum information checklist for SNP reporting ..................................... 85 Table 3.6 NGBO Compliance with OBO Foundry Principles ...................................................... 87 xvi List of Figures Figure 1.1 Classification of data integration systems. .................................................................. 12 Figure 1.2 Dimensions of the data integration approach. ............................................................. 13 Figure 1.3 Challenges for omics data sharing. .............................................................................. 17 Figure 1.4 Missing contextual data fields across genomics records in EGA datasets. ................. 23 Figure 1.5 FAIR data principles.................................................................................................... 24 Figure 2.1 An example of PID representing the “biobank.” ......................................................... 46 Figure 3.1 NGBO Ontology Development Workflow. ................................................................ 63 Figure 3.2 Set of ontology namespaces used in NGBO................................................................ 71 Figure 3.3 (A) Illustration of the omics data generation assay. (B) Illustration of the bioinformatics analysis and a selection of possible pipeline components. ................................... 75 Figure 3.4 Illustration of the approach implemented using NGBO to improve the discovery of SHGP data and the FAIRness of the data. .................................................................................... 90 Figure 3.5 Using NGBO for specimen tracking and traceability. ................................................. 94 Figure 3.6 Using NGBO for data privacy and user access controls. ............................................ 96 Figure 4.1 Excel sheet template. ................................................................................................. 116 Figure 4.2 Architecture of the Federated Query Engine. ............................................................ 118 Figure 4.3 Illustration of the initial login page. .......................................................................... 119 Figure 4.4 Illustration of the filter selection. .............................................................................. 119 Figure 4.5 Visualization of the query results. ............................................................................. 120 xvii Figure 5.1 Integration of ontology, annotation tool, and NLP .................................................... 133 Figure 5.2 Example of information extracted from physician note ............................................ 137 Figure 5.3 An example of semantic relation extraction. ............................................................. 142 Figure 5.4 PRISMA-style flow diagram illustrating the identification, screening, eligibility assessment, and final inclusion of studies in the review. ............................................................ 146 xviii List of Abbreviations ACMG: American College of Medical Genetics API: application programming interface BAM: Binary Alignment Map BAP: Biorepository Accreditation Program BBMRI-ERIC: Biobanking and BioMolecular resources Research Infrastructure CAP: College of American Pathologists CQ: Competency Questions DL: Description Logic DNA: Deoxyribonucleic acid EGA: European Genome-phenome Archive EHR: Electronic Health record ETL: Extraction, Transformation, and Loading FAIR: Findability, Accessibility, Interoperability, and Reuse FAIR4RS: FAIR principles for Research Software GA4GH: Global Alliance for Genomics and Health GDPR: General Data Protection Regulation GO: Gene Ontology GSC: Genomic Standards Consortium GWAS: Genome-wide association studies xix HIPAA: Health Insurance Portability and Accountability Act IAO: Information Artifact Ontology ICO: Informed Consent Ontology INSDC: International Nucleotide Sequence Database Collaboration IRB: institutional review board ISBER: International Society for Biological and Environmental Repositories ISO: International Organization for Standardization IT: Information Technology MDD: Major Depressive Disorder MDL: Medical Diagnostic Laboratory MIABIS: Minimum Information About Biobank Data Sharing MIAME: Minimum Information on the Microarray Experiments MIAPE: Minimum Information About Proteomic Experiments MIGS: Minimum Information About a Genome Sequence MIxS: Minimum Information about any (x) Sequence ML: Machine learning NGBO: Next Generation Biobanking Ontology OBI: Ontology for Biomedical Investigations OBIB: Ontology for Biobanking OBO: Open Biological and Biomedical Ontologies OECD: Organization for Economic Co-operation and Development xx OMIABIS: Ontologized Minimum Information About Biobank Data Sharing OWL: Web Ontology Language PDF: Portable Document Format PHIPA: Personal Health Information Protection Act PID: persistent identifier PII: Personally Identifiable Information RDF: Resource Description Framework RDFS: Resource Description Framework Schema RO: Relation Ontology SOP: Standard Operating Procedure SPARQL: SPARQL Protocol and RDF Query Language SRA: Sequence Read Archive UMLS: Unified Medical Language System UNESCO: United Nations Educational, Scientific, and Cultural Organization VCF: Variant Call Format W3C: World Wide Web Consortium WGS: Whole Genome Sequencing xxi Acknowledgements I would like to acknowledge and express my deepest appreciation for my supervisor Dr. William Hsiao, who made this study possible. Words cannot express my gratitude for his patience and his academic and mental support. I would also like to thank my supervisory committee members, Dr. Mel Krajden, Dr. Rachel Pottinger, and Dr. Elodie Portales-Casamar, who genuinely provided their knowledge and expertise. Additionally, this study could not have been performed without the support of King Fahad Medical City, which funded my Ph.D. Furthermore, I am so grateful to have the CIDGOH lab members as my lab mates. You provided a friendly working environment, and I’ve learned a lot from everyone. My deepest gratitude to my caring, loving, and supportive husband, Mansur Aloleany. He supported me every step of the way with patience, humor, and love. My beautiful kids Yasmina and Naif made this journey much better. I love you more than you will ever know. My siblings and their families, who have always loved me unconditionally, taught me to work hard for things I aspire to achieve; everything you have done for me is truly appreciated. A big thank you to Hanin and Abdulrahman; you have always been there for me throughout my stay in Vancouver (on my good and bad days), and I truly don’t know what I would have done without you. xxii Dedication To my parents, I hope this achievement completes the dream you always had for me 1 Chapter 1: Biobanks and big data sharing, an open-ended issue 1.1 Author summary Biobanks play a fundamental role in preserving and managing bio-specimens and their related data, serving as invaluable repositories for biomedical research (1). Biobanks support critical advancements in research by offering access to high-quality bio-specimens, the data derived from them, and associated metadata (2,3). These bio-specimens—which contain information about the body, including genes and proteins—provide biomedical researchers with key insights into disease biology (4). Despite recognizing the value of biobanks, researchers still anticipate or encounter difficulties in sourcing and using bio-specimens. A study published by Lawrence et al. (5) identified perceptions of governance barriers to the discovery, access, and use of bio-specimen among many researchers. One of the main challenges to accessing samples was, in fact, locating the samples, as the promise of data sharing and re-use can only be realized if the correct specimen/data can be found in the first place. Researchers also pointed to the “lack of linked data'' such as clinical or contextual data as another obstacle to using bio-specimens (5). Furthermore, in some cases, data integration from different biobanking sources is also required, but the integration process presents additional challenges (6). In genomic studies of diseases with highly diverse molecular subtypes, such as cancer, a single biobank may not provide enough specimens and data for specific cancer subtypes due to the heterogeneous molecular characteristics, making it necessary to combine data from multiple biobanks (7,8). Biobank data 2 models and query engines from different organizations are often created independently on the fly. Also, biobanks use different data representation formats, further hindering the data discovery and integration process. All these challenges hinder specimen and data findability, accessibility, interoperability, and reusability. This chapter reviews the current state of biobanks in terms of digitization and outlines the challenges that remain open. 3 1.2 Introduction In recent years, digital innovation and data-intensive fields such as omics, machine learning, and precision (aka personalized) medicine have transformed biomedical research by enabling large-scale data analysis, uncovering complex disease mechanisms, and tailoring treatments to individual patients. This progress has facilitated breakthroughs in disease modeling, precision diagnostics, and patient-specific therapeutic strategies (9–12). One of the factors contributing to the success of these fields is access to large collections of high-quality bio-specimens and data (13), which enable researchers to conduct large-scale studies to investigate complex diseases and validate their findings through experimental and computational methods. Biobanks, by systematically collecting, storing, and curating these resources, provide the essential foundation for advancing scientific research, discovery, and innovation (14). Bio-specimens, the cornerstone of biobank resources, are samples of tissue, blood, urine, or other biologically derived materials collected during medical procedures like biopsies or blood draws. The utilization of bio-specimens helps advance our understanding of biomedical research and health care in many ways, such as improving our understanding of diseases, biomarker discoveries, supporting drug development, and enhancing medical procedures. One notable example of how bio-specimens drive biomedical innovation is the development of liquid biopsy techniques. By analyzing blood plasma from cancer patients, researchers have created non-invasive methods for tumor monitoring and early relapse detection, significantly improving patient outcomes (15). 4 The value of bio-specimens in biomedical research becomes even more apparent when addressing the complexity of human diseases. Many conditions, such as cancer, Alzheimer's, and cystic fibrosis, are characterized by heterogeneity—variations in genetic and molecular profiles that contribute to similar clinical manifestations. (10–12). This diversity presents a significant challenge, as understanding and addressing such diseases require comprehensive insights into their underlying mechanisms, which in turn demands access to a larger number of diverse and high-quality bio-specimens (13). Accordingly, biobanking has gained importance in biomedical research, as biobanks provide access to many bio-specimens and data that can be used for research with fewer resources required from the researchers. Bio-specimens are associated with two key types of data. First, there is sample accompanying data, such as demographic information, which provides context to the bio-specimens. Second, data generated through analysis, such as high-throughput omics sequencing, are collectively referred to as bio-specimen-derived data. Omics refers to high-throughput molecular-level data generated from fields such as genomics, transcriptomics, proteomics, and metabolomics. These fields aim to provide a comprehensive view of biological processes by measuring genes, transcripts, proteins, or metabolites at scale (16). Advances in molecular technologies—such as next-generation sequencing (NGS), microarrays, and mass spectrometry—have made it possible to generate these large datasets quickly and cost-effectively (17). Together, these data provide key insights into genetic, molecular, and cellular processes, forming the basis for developing innovative diagnostics and treatments and plays a central role in many biomedical research activities. For instance, in cancer research, approximately 50% of published studies rely on 5 human bio-specimens or their derivatives, enabling earlier detection of cancer and the development of more effective treatments (4). In recent years, omics technologies have become essential in both research and clinical settings, particularly in disease profiling and precision medicine (18). However, the high volume and complexity of omics data require detailed contextual information to make the data meaningful and reusable. This includes metadata about how specimens were collected, processed, analyzed, and interpreted (19). As the role of biobanks expands in parallel with advances in omics and precision medicine, it becomes increasingly important to understand how these resources are defined, organized, and standardized. A closer examination of biobank structures, their historical development, and global implementation reveals how they serve as foundational enablers of modern biomedical research. However, this potential is not without challenges. Issues related to data interoperability, standardization of metadata, and the integration of diverse data types continue to limit the efficient reuse and discovery of biobanking resources. Additionally, ensuring the quality and consistency of biobank operations—particularly in documenting how specimens and specimen derived data are collected, processed, stored, and documented—requires adherence to established accreditation and regulatory frameworks. Addressing these challenges necessitates not only technical solutions but also the incorporation of biobanking and clinical laboratory standards into data models and metadata frameworks. The following sections delve into these dimensions, highlighting both the progress made and the persistent obstacles. 6 1.2.1 Biobanks: definition and background Biobanks play a crucial role in biomedical research by enabling the systematic collection, management, and storage of bio-specimens and their associated data for research purposes (20,21). Initially, biobanks were small repositories created to address the specific needs of individual research projects. Over time, they evolved into large institutional and government-supported repositories, as well as commercial and population-based biobanks. These repositories now systematically collect a wide range of specimens, including organs, fluids, tissues, and cells, which can be categorized based on various factors, such as race, population, study type, disease stage, and patient profiles (22). There are many different forms of biobanks (14,23,24), and different organizations define biobanks based on their specific focus (25,26). According to The Organization for Economic Co-operation and Development (OECD), biobank is a collection of biological material and the associated data and information stored in an organized system, for a population or a large subset of a population. The International Society for Biological and Environmental Repositories (ISBER) definition of biobank is “Infrastructure for the collection, archiving, and storage of bio-specimens and their associated data, and the procedures and related services connected to the specimens and associated data.” In this thesis, I follow a broader definition, the International Organization for Standardization (ISO) 20387:2018 biobanking definition, which states that “a biobank is an organization or infrastructure that performs biobanking activity.” (27). This definition aligns best with the research scope as it encompasses both the physical and digital aspects of biobanking activities, including data management, integration, and discovery—key 7 themes of this work. Additionally, while biobanks collect bio-specimens that originate from various sources, including humans, animals, plants, and the environment, this thesis focuses exclusively on banking human bio-specimens. In recent years, numerous biobanks have been developed globally, significantly contributing to biomedical research. For example, the Biobank Graz (28) is one of the world's largest and most well-known clinical biobanks, storing approximately 20 million tissues and associated data, blood, and Deoxyribonucleic Acid (DNA) samples from over 1.2 million patients. The UK Biobank, which contains in-depth genetic and health information from half a million UK participants, is another well-known example. Both biobanks provide specimen and data access to comprehensive disease control efforts and progress in disease prevention, diagnosis, and treatment, as well as supporting various disease-related research. 1.2.2 From bio-specimens to big data As biomedical research continues to grow more data-intensive, the integration of large-scale bio-specimen collections with standardized clinical and laboratory data has become essential (29). This integration not only enables advances in fields such as genomics and precision medicine (30,31) but also facilitates a deeper understanding of complex biological systems. 1.2.2.1 The need for large scale data Biological systems are inherently complex due to the intricate interactions between genetic, molecular, and environmental factors. This complexity manifests in countless processes, such as 8 gene regulation, protein interactions, and cellular signaling pathways, which often vary across individuals and populations. Fully understanding these systems often requires access to large datasets that enable researchers to identify patterns, correlations, and subtle nuances that smaller datasets often overlook. These large datasets also provide the statistical robustness needed for reliable conclusions, reducing the risk of findings being artifacts of limited sample sizes. The impact of large datasets is particularly evident in the field of genomics, where the ability to identify disease-associated variants often hinges on the size and diversity of the dataset. Genome-Wide Association Studies (GWAS), a method used to identify genetic variants associated with specific diseases, have been instrumental in uncovering key insights but face limitations when dealing with diseases characterized by a polygenic architecture, where numerous genetic variants each have small individual effects. For example a study by Nishino et al.(32) highlighted the challenges of GWAS in studying Major Depressive Disorder (MDD), a condition characterized by many small-effect risk variants. The study demonstrated that the lack of statistically significant findings in MDD research stemmed from insufficient sample sizes. The authors suggested that datasets comprising at least 50,000 MDD cases and an equal number of controls would significantly improve the ability to identify disease-associated variants. This underscores the critical role of large-scale data in uncovering the genetic underpinnings of complex diseases. This example illustrates the broader challenge of studying complex diseases, where large-scale datasets are essential to uncover subtle genetic associations. As the field of genomics continues to advance, the availability of diverse and expansive datasets will remain 9 pivotal in addressing similar challenges across a wide range of conditions, driving progress in understanding disease mechanisms, and enabling the development of therapies. 1.2.2.2 Shift towards data-centric biobanks In response to the increasing importance of large-scale data in biomedical research, biobanks have evolved into data-centric repositories, integrating digital assets with traditional bio-specimen storage (33). Modern biobanks are no longer limited to storing specimens in freezers and liquid nitrogen tanks. Instead, they have embraced emerging technologies, transforming into large-scale repositories that house data from hundreds of thousands of participants (34). These repositories combine specimen storage with extensive digital datasets, including specimen-derived data (e.g., genomics and imaging), clinical histories, and lifestyle information, facilitating diverse studies such as association analyses (35,36). For example, the UK Biobank, one of the largest and most comprehensive biobanks, securely stores data from more than half a million participants. A significant portion of this data is derived from advanced technologies like genomic sequencing. As of November 2023, the UK Biobank has completed whole genome sequencing (WGS) for all 500,000 participants, making it one of the largest resources for genetic and health-related data in biomedical research (37). 1.2.3 Challenges in modern data-centric biobanks Although the practice of banking samples for research purposes is not new, and the integration of digital data into bio-repositories has become increasingly common, key challenges remain in terms of utilizing modern biobanking resources (24,38,39). Among these challenges is the 10 discovery of relevant specimens and datasets. The value of biobanking resources can only be actualized if the material of interest can be located and made available to be used (40). With the data physically distributed across institutions, housed in silos, and stored in varying structures and formats, researchers often spend significant time locating, negotiating access to, and working with biobank data before beginning their actual research (41). Addressing these issues requires robust tools for interoperability and integration.(42). Interoperability enables different systems and software to exchange and use information while preserving its meaning and context (43). Integration, on the other hand, involves merging data from multiple sources into a single, unified view, allowing researchers to work with comprehensive datasets more efficiently (44). Together, these capabilities are essential for overcoming data silos, enabling data discovery, and facilitating seamless collaboration across institutions. Another major challenge is the lack of Persistent Identifiers (PIDs) for cataloging and linking bio-specimens and their associated data across repositories. Many biobanks use locally assigned identifiers, which are unique within their own systems but are not globally standardized. This makes it difficult to track and integrate specimens across multiple sources, often leading to inconsistencies in data discovery and interoperability. A standardized approach using PIDs would enhance the ability to trace, reference, and integrate biobank resources, ensuring more efficient data utilization. 1.2.3.1 Data integration models and current challenges The integration of data from biobanks is further complicated by the diversity of data collection, management, and storage practices. Many biobanks operate independently, adopting unique formats, terminologies, and protocols that hinder seamless data integration (14,23,24). For 11 instance, differences in how contextual data, such as specimen processing details or sequencing quality metrics, are documented can create barriers to standardizing and harmonizing datasets. Data integration systems are, therefore, a prerequisite for greater access and better data processing. These systems produce a unified data view, regardless of the original source or format. Figure 1.1 presents different classifications of data integration systems. Additionally, Figure 1.2 further categorizes these systems according to three dimensions: autonomy (the extent of independence retained by each biobank in controlling their data), distribution (the physical distribution of data across locations), and heterogeneity (differences in data models and implementations across systems). 1.2.4 Challenges in data standardization and integration Beyond locating and accessing data, researchers face additional challenges in integrating biobank data due to the lack of standardization. Without consistent formats and structures, data elements from different biobanks are difficult to compare and integrate. For example, variations in the documentation of sequencing quality metrics or the bioinformatics pipelines used for sequence variant calling can result in datasets that are not directly comparable. Quality scores, such as Phred scores, might be interpreted differently depending on the sequencing pipeline or software version used, introducing variability in downstream analyses. Similarly, variant calling algorithms such as Genome Analysis Toolkit (GATK)(45), or the Sequence Alignment/Map Tools (SAMtools)(46) apply different thresholds and algorithms to identify genetic variants, leading to inconsistencies in the reported results for the same dataset. Addressing these issues requires a focus on data standardization, harmonization, and the establishment of minimum 12 information requirements to ensure data interoperability and reusability of the data. High-quality specimen-associated data are essential for defining both the patient characteristics and the diseases (47–49). Standardization ensures that clinical information (e.g., medical diagnoses, medical images) and biological data (e.g., gene and protein sequences, functions, biological processes, and pathways) follow consistent formats, making them easier to integrate and compare. Harmonization aligns and reconciles data from diverse and heterogeneous sources, enabling compatibility across datasets collected under different conditions or protocols (50). Figure 1.1 Classification of data integration systems. As shown in panel (1), the first type of data integration system loads heterogeneous data from data sources to centralized storage after transforming them into a common data representation format. Such systems require the extract, transform, and load (ETL) process, which generates copies of the data and also prevents biobanks from retaining complete control over their data. The second type of data integration system, as shown in (2), supports data distributed across networks; however, the support is provided only if the data sources in the system are homogeneous in terms of the data model and access methods. The third type of data integration system, as shown in (3), supports data heterogeneity among data sources in the integration system. It again requires biobanks to create copies of sensitive data and prevents biobanks from retaining complete control over their data. Finally, the fourth type of data integration system, as seen in (4), supports data distribution across networks and the heterogeneity of data sources. These systems have a global schema, for example, an ontology, which then acts as a mediator for reconciling heterogeneity between different sources considered in the integration process (51). 13 Figure 1.2 Dimensions of the data integration approach. The autonomy dimension characterizes the degree to which each biobank (data owner) in the integrated system operates independently. In other words, data owners retain complete control over the data model and schema. Furthermore, in many cases, data owners can join or leave the integration system at any time, and the data fragments that should or should not be accessible by the integration system are also defined by the owners. The distribution dimension characterizes the physical distribution of the biobanking data across different locations; such a distribution can be accessed through an appropriate application programming interface (API). The heterogeneity dimension characterizes different models of the same real-world entities and different technical implementations; “specimen source,” for example, could be defined as the physical location (center) that provides the specimen or the anatomical location of the specimen in the body. 1.2.4.1 Global efforts to address biobanking data standardization Over the past two decades, international organizations have made significant efforts to enable data interoperability and integration in biobanking by coordinating, harmonizing, and standardizing information related to the collection, access, and research activities associated with biological resources and human data (52). Best practices guidelines for human and bio-specimen 14 resources were published by organizations such as the College of American Pathologists (CAP), OECD, ISBER, United Nations Educational, Scientific, and Cultural (UNESCO), and the European research infrastructure for Biobanking (BBMRI-ERIC). These organizations aim to promote the availability of high-quality biological and environmental specimens for future research by addressing scientific, technical, legal, and ethical issues relevant to specimen repositories. For example, ISBER’s Best Practices and the OECD’s Guidelines on Human Biobanks and Genetic Research Databases (53) provide general recommendations, typically specifying reporting requirements presented as tables or checklists of contextual data to accompany specimen-derived data. However, these guidelines do not include recommendations or standards for data structure or format. BBMRI-ERIC introduced the Minimum Information About Biobank Data Sharing (MIABIS) framework to improve access to biobanks of various formats, harmonize standards, and establish operational procedures that address ethical, legal, and societal considerations. The framework consists of two main components: First, there is a set of minimum information requirements, and second, a data format, as the information about the bio-specimens needs to be in an appropriate format that supports data integration. Minimum information requirements specify the critical data elements and contextual data needed to ensure the data’s usability, such as contextual information about specimen collection, processing, and associated clinical details. Together, these processes support the integration of clinical and biological data, which is crucial for advancing precision medicine and translational research. High-quality, harmonized data are also a prerequisite for maximizing the value of biobanks through effective national and international 15 collaborations (54–56). The MIABIS community standards work on several levels of granularity, intending to support interoperability between biobanks sharing their data. General attributes to describe biobanks, sample collections, and studies at an aggregated/metadata level are defined in MIABIS Core 2.0 (57). While initiatives such as MIABIS and ISBER provide frameworks for standardizing biobank metadata, they do not enforce the use of PIDs for uniquely referencing specimens and data resources. As a result, researchers often struggle to connect datasets across repositories due to inconsistencies in their identifier schemes. The adoption of PIDs would complement existing standardization efforts by ensuring that every specimen, dataset, and metadata record can be uniquely identified and traced over time, improving data accessibility and integration. 1.2.4.2 Global efforts to address omics data standardization In the previous sections, the challenges of data discovery and the lack of standardization in biobanking were discussed. This section focuses specifically on omics data, a critical component of modern biobank resources. Omics data is increasingly being generated not only in advanced research laboratories but also in clinical diagnostic laboratories, hospital systems, and public health agencies. With the growing adoption of technologies like next-generation sequencing, the production of omics data has become routine in both research and clinical settings. This has led to the generation of extremely large volumes of data, prompting efforts to establish standardized file formats such as FASTQ (for raw sequencing reads and their quality scores), BAM (Binary Alignment/Map, for storing aligned sequence reads), and Variant Call Format (VCF). While 16 standardizing file formats has helped address challenges related to data exchange, storage, and processing, the greater challenge lies in the contextual data—the information describing how the data was generated, processed, and interpreted. Researchers encounter significant barriers during the discovery and sharing of specimen-derived omics data, as shown in Figure 1.3. One major challenge is the incompatible representation of contextual data, which refers to how data is stored, processed, and transmitted. Contextual data provides the necessary background information to relate disparate pieces of data, enabling the interpretation of specimen-derived data in downstream analyses (36,58). For example, contextual data for a DNA sequence file may include experimental interventions associated with the specimen, collection protocols, nucleic acid extraction methods, library preparation details, sequencing platforms, bioinformatics workflows, and associated software. To enable data discovery and reuse, it is essential that this information is well-annotated and stored in a computer-accessible structure (i.e., semantically coded). Each data item should include minimal machine-readable metadata, such as a PID, enabling machines to answer complex queries like, “Search for disease X omics-derived data Y in repositories A, B, and C with a mutation in gene Z.” 17 Figure 1.3 Challenges for omics data sharing Panel A illustrates a common practice in the clinical laboratory of free-unstructured text as documented as part of a clinical exome test report. The text includes contextual data that is critical for the secondary use of the data, such as sequencing technologies used, variant classification protocols, and information on the reference genome used in the analysis. Panel B summarizes the issues and challenges of using free-unstructured text in reporting omics data generation. The colored pentagons represent the issue regarding concepts represented in the gray rectangles. Due to a lack of interoperable standards, it is often recorded as free text and may contain incomplete or missing information. The collected data are often insufficient for downstream analysis and missing pieces of minimum information are required. As the data lack machine-accessible identifiers, locating them is a labor-intensive process. A complete, coherent, and standard description of contextual data maximizes the likelihood of finding and sharing the data. A B 18 Research communities have made significant progress toward standardizing contextual data for genomics, transcriptomics, and proteomics (58,59). For instance, the Genomic Standards Consortium (GSC) developed the Minimum Information about any (x) Sequence (MIxS) standard, shown in Table 1.1 (60). The GSC has also introduced international standards such as the Minimum Information about a Microarray Experiment (MIAME) for microarray data (61), the Minimum Information about a Genome Sequence (MIGS) (62), and the Minimum Information about a Proteomic Experiment (MIAPE) (63). While the previous discussion focused on biobank-specific and research setting challenges, similar issues exist in the clinical settings—particularly where clinical omics data are integrated with biobank repositories. Clinical data generated during routine healthcare activities presents similar challenges in terms of standardization and integration. Clinical laboratories produce significant amounts of genomic data for diagnostic purposes, yet much of this data holds potential value beyond its initial use. A common issue is that Standard Operating Procedures (SOPs) and protocols are often stored in unstructured textual formats (e.g., Portable Document Format (PDF) or Word documents), which hinders data integration and querying. For example, automating queries to locate specific data, such as “Find data format X for omics-derived data Y related to disease Z filtered by life stage,” becomes nearly impossible when critical metadata is embedded in free text. Similarly, extracting information on sequencing coverage depth from statements like “the whole exome is sequenced on the Illumina HiSeq 2500 sequencing system 19 with a minimum coverage of 30x for 95% of target regions” requires manual parsing, as these details are not machine-readable. An assessment by Gonçalves and Musen showed that even basic features, such as organism type and sex, are not appropriately standardized in major data repositories and hence, are difficult to parse with basic queries (64). Moreover, given that protocols and SOPs are revised or updated over time, problems will be repeatedly encountered when attempting to reuse the data while the associated contextual data is being replaced. A list of minimum information requirements and a detailed description of how a specimen was processed, analyzed, and linked directly to the specimen, together with an indexed, searchable format, is therefore essential to ensure data discovery so it can be easily found, verified, analyzed, and interpreted by the wider scientific community. The second challenge concerns incomplete or missing values for specific contextual data fields (65). Missing or poorly described data are still common across omics studies. Such instances are typically observed when the values of the variable of interest are not recorded or measured for all subjects in the study sample (66). This is exemplified when data submitters provide partial or mismatched contextual data, either leaving blank fields or filling in NA, missing, or unknown. As shown in Figure 1.4, the “technology” used to generate the data is not annotated for all samples submitted to the European Genome-phenome Archive (EGA) for sequencing records. Inevitably, missing data severely limits data reusability. To ensure that datasets remain useful and interoperable, contextual data must be enriched with structured metadata that explicitly links 20 datasets to their relevant attributes. To overcome the challenges posed by incompatible data formats, missing contextual data, and incomplete metadata, the application of the FAIR (Findable, Accessible, Interoperable, and Reusable) Data Principles (67) is essential. By adhering to these internationally recognized guidelines, biobanking repositories can ensure that their data is discoverable, shareable, and usable across different platforms and research contexts. Figure 1.5 presents the FAIR guiding principles. Additionally, it is important to note that both data standards and minimum information requirements are essential. Data standards provide structured frameworks for organizing and sharing data, while minimum information requirements ensure that essential metadata elements are consistently captured. Without both, datasets remain difficult to compare, integrate, and reuse across different studies. Missing contextual data affects the process of locating and reusing the data, regardless of whether the information is available elsewhere, such as data silo. Data silo is a collection of information isolated from and not accessible by other parts of an organization. Even though we are in the era of big data in the field of biomedical research, we continue to gather data in silos. The challenges of using data silos include, a) multiple data management systems used by the same organization. Some of them cannot be integrated with other systems. (b) data collection approaches are neither standardized nor consistent and are time-consuming and labor-intensive to manage, clean, and analyze; (c) incomplete or missing data 21 In most cases, complete contextual data is available only as part of unstructured paragraphs in a textual document such as a clinical report, manuscript, or SOP, and is often stored in silos (68,69); a situation that inherently creates data fragmentation and weakens data integrity. For example, when bioinformatics analysis and pipeline descriptions are stored in data silos or code repositories, which may or may not contain human readable instructions necessary to use it and set it up, understanding how to invoke a software component, prepare data in the format it requires, and use it in conjunction with other software typically takes a significant amount of time (70). Contextual data of bioinformatics software is crucial for understanding, reusing, and reproducing results in biomedical research. The scientific community started to take initiatives such as the software carpentry (https://software-carpentry.org/) events, which teach researchers about best practices for software documentation and description; community groups such as FAIR principles for Research Software (FAIR4RS), which actively addresses how to evolve the FAIR principles for research software (71); code repositories such as GitHub provide functionality to store and version codes. Based on the available software metadata documentation recommendations, and the CAP requirements. It is recommended to document contextual data variables from the inputs, outputs, steps, parameters, and important specimen identifiers associated with a bioinformatics pipeline and relevant pipeline-specific metadata (i.e., version, run ID). These details should be documented in a standard format so that this information can be computationally accessed and queried. 22 Label Definition sample collector sample ID The user-defined name for the sample. sample collected by The name of the agency that collected the original sample. sequence submitted by The name of the agency that generated the sequence. sample collection date The date on which the sample was collected. geo_loc_name (country) The country where the sample was collected. geo_loc_name (state/province/territory) The province/territory where the sample was collected. organism Taxonomic name of the organism. isolate Identifier of the specific isolate. isolation source The material sampled (this information is encoded by 6 additional fields that need only be filled as applicable, depending on sample type; anatomical material, anatomical site, body product, environmental material, environmental site, collection device, collection method). host (scientific name) The taxonomic or scientific name of the host. host disease The name of the disease experienced by the host. host age Age of host at the time of sampling. host gender The gender of the host at the time of sample collection. sequencing instrument The model of the sequencing instrument used. consensus sequence software name The name of the software used to generate the consensus sequence. consensus sequence software version The version of the software used to generate the consensus sequence. Table 1.1 MIxS Compliance and Implementation Metadata Standards 23 Figure 1.4 Missing contextual data fields across genomics records in EGA datasets Here, it is likely that a bioinformatician might run into challenges when interpreting data that comes from different platforms due to differences in read lengths and accuracy. The appropriate annotation of information on the technology used in producing data is essential for the data discovery process to enable the search and retrieval of the data. The appropriate quality control and secondary use of data are also crucial prerequisites. 24 Figure 1.5 FAIR Data Principles This figure illustrates the four foundational FAIR Data Principles—Findable, Accessible, Interoperable, and Reusable—which guide best practices in data management to enhance the discoverability and utility of data in scientific research. Each principle is broken down into actionable sub-principles. The Findable component emphasizes the importance of assigning globally unique and persistent identifiers (F1), using rich metadata (F2), linking metadata to their described data (F3), and ensuring data are registered or indexed in searchable resources (F4). The Accessible principle ensures that metadata and data can be retrieved using standardized communication protocols (A1), with support for authentication and authorization if required (A1.2), and that metadata remain accessible even if the data are no longer available (A2). Interoperable data rely on the use of shared formal languages (I1), community-recognized vocabularies that conform to FAIR standards (I2), and proper referencing to related metadata (I3). Finally, the Reusable principle ensures that metadata are richly described (R1), associated with clear usage licenses (R1.1), contain provenance information (R1.2), and comply with domain-specific community standards (R1.3). These principles collectively support machine-actionable and human-understandable data sharing and reuse.25 In summary, biobanks play a crucial role in uncovering how diseases arise and progress (known as disease etiology), identifying potential biomarkers, and advancing precision medicine. However, despite their significance, there remains a notable gap between the establishment of biobanks and the realization of their full potential. This thesis addresses the key challenges hindering the discovery and reuse of biobanking resources, particularly in the era of digitization. It focuses on how semantic web technologies introduced in Chapter 2—such as ontologies, RDF, and SPARQL—can enhance biobank utility and accelerate research. The main objectives of this work are outlined below: (1) Introduce omics contextual data into biobanking ontologies: This thesis incorporates omics contextual data into biobanking ontologies and leverages PIDs to uniquely identify and link entities. This approach facilitates the standardization of contextual data representation and enhances the discovery of relevant bio-specimens and associated datasets. By addressing the challenge of locating and accessing bio-specimens, this objective improves the efficiency of research workflows. It involves creating an application ontology that facilitates data integration and harmonization. (2) Integrate Biobanking and Clinical Laboratory Accreditation Requirements: Accreditation bodies, such as CAP Biorepository Accreditation Program (BAP), establish guidelines for processes including data collection, processing, and storage, and documentation to ensure quality. This thesis incorporates and ontologizes these documentation requirements, contributing to the development of a minimum information standard for reporting wet and dry lab activities. 26 (3) Support Data Federation: As biobank users face an ever-growing array of applications and data resources, this thesis explores semantic web approaches to facilitate seamless data access and integration across diverse systems. (4) Ensure Privacy and Access Control: Privacy protection and user access control are critical issues in managing biobanking data. This thesis employs semantic web functionalities to implement data granularity rules and access control mechanisms. During the work presented in this thesis, I noticed the urgent need for significant methods and recommendations for extracting biomedical knowledge from unstructured documents and mapping it to structured knowledge. In chapter 5, I conducted a literature review of leveraging ontologies for biomedical text mining by focusing on tasks and methods developed over the past years. 27 1.3 Discussion This chapter has explored the digitization of biobanking, highlighting the progress made in data management approaches and tools that enable researchers to extract meaningful insights from biobanking resources. However, realizing the full potential of biobanks requires robust frameworks and policies that address the challenges of data sharing and reuse. Key considerations include the development of compatible data representations, standardized identifiers for biobank entities, and comprehensive minimum information requirements for digital assets. Additionally, advanced data integration models, discovery mechanisms, and federated platforms are essential for ensuring seamless access while preserving privacy and access control for the individuals to whom the specimens belong. Looking forward, there is an urgent need to establish a more sophisticated biobanking infrastructure and data ecosystem that can effectively utilize the vast amounts of existing biobanking data. Federated and innovative models that integrate data from heterogeneous sources have the potential to unlock new opportunities in biomedical research and healthcare. These systems must carefully balance privacy and de-identification with the broader purpose of biobanks, ensuring ethical stewardship of data while maximizing its value for scientific discovery. 28 Chapter 2: Setting the context: motivation and semantic state-of-the-art 2.1 Author summary In Chapter 1, I provided an overview of the evolution and significance of biobanks in biomedical research, highlighting their transition from physical repositories of bio-specimens to data-centric models. I also discussed the challenges associated with integrating and sharing biobank data, particularly in the context of omics data and distributed repositories. As the complexity of biobanking data grows, traditional methods of managing and querying this data are no longer sufficient. Semantic web technologies provide an effective solution for harmonizing, discovering, and integrating biobank data, especially when dealing with distributed repositories and vast amounts of omics data. In Chapter 2, I will specifically explore, from a technical perspective, key gaps in current approaches to biobank data management, focusing on the importance of persistent identifiers and examining technical challenges related to data granularity and the implementation of FAIR principles in genomic data. By addressing these gaps, I aim to outline the foundational challenges that need to be resolved to unlock the full potential of biobank resources for biomedical research. 29 2.2 Introduction Currently biobanks are composed of some or all of the following components: (1) a collection of bio-specimens; (2) contextual data describing the biobanks and stored bio-specimens; (3) data accompanying bio-specimens (e.g. medical records); (4) data derived from bio-specimens (e.g., omics data and imaging data); and (5) expertise in various fields related to the long-term preservation and analysis of biological materials (72–74). These components reflect the complexity of managing digital information about various entities—such as specimens, devices, people, digital assets, and organizations—within biobanks. As highlighted in Chapter 1, to achieve the maximum utilization of biobanking digital assets, it is necessary to establish efficient methods and tools to enable effective information discovery and exchange across heterogeneous biobanking sources and users (75,76). These tools also need to take into consideration several layers of technical and non-technical measures required to implement efficient and ethical information discovery, usage and exchange methodology (77). Without such measures, researchers face barriers in accessing and integrating data, which limits the potential impact of biobank resources. A critical aspect of data sharing and reuse lies in the proper annotation of contextual data. Annotations add clarity and meaning to data by providing essential context, including details on resource origin, purpose, and characteristics. These annotations not only improve researchers’ understanding of available data but also enhance data discovery and retrieval. By attaching standardized terminologies, identifiers, or descriptors to contextual data, annotations enable both researchers and machines to efficiently locate and access relevant information. Consequently, 30 annotations play a pivotal role in enabling data to be found, understood, processed, and reused while maintaining its integrity and provenance (6,41,78). This chapter focuses on identifying and addressing gaps in the quality and management of contextual data within biobanks. The discussion begins with an overview of foundational semantic web technologies and linked data concepts, which offer innovative approaches to managing and sharing biobank data. This is followed by an examination of unresolved challenges in existing research and practices, particularly in the context of enabling structured, comprehensive, and FAIR data. 2.2.1 Preliminaries: key technologies (semantic web and a web of linked data) Modern biobanks store increasingly large and complex datasets, but traditional data management approaches struggle with scalability and integration. These challenges stem from the need to efficiently link and standardize data across multiple domains, such as genomics, clinical records, and environmental factors. To address this, semantic web and linked data technologies provide a standardized framework for data representation, enabling interoperability, contextual enrichment through controlled terminologies, and advanced querying across diverse datasets. By leveraging semantic web technologies such as ontologies, Web Ontology Language (OWL), and Resource Description Framework (RDF), biobank data can be structured in a machine-readable format, facilitating integration, harmonization, and automated reasoning. These technologies combine the machine-interpretable features of the semantic web with the internet-31 accessible interfaces of web services, enabling seamless interactions between different data systems (79). Some of the key features of semantic web technologies include: ● Expressivity: Semantic web technologies, like RDF and OWL, are tools designed to represent and connect different types of data in a meaningful way. For example, in biobanking, a blood sample (bio-specimen) can be connected to the donor’s medical history, lifestyle factors (like smoking habits), and the results of genetic tests (omics data). RDF organizes these pieces of information as distinct "nodes" in a network, while OWL specifies how these nodes are related—for instance, a blood specimen can be described as originating from a specific organism, being the result of a defined specimen collection process, and deriving from blood tissue. This structured representation allows biobanking data to be interconnected meaningfully, linking specimens to their source, collection methods, and subsequent analyses. Such clarity and detail help researchers interpret complex datasets and gain valuable insights. ● Formal semantics: Formal semantics is “an approach to semantics, the study of meaning, with roots in logic, the philosophy of language, and linguistics” (80). In the context of the semantic web, formal semantics explicitly defines the relationships between different data elements, enabling computers to interpret and reason about the data with precision (81,82). In biobanking, formal semantics provides a clear framework for representing complex biomedical processes. For example, "bioinformatics analysis" can be defined as a process with specific objectives, such as analyzing DNA sequences, which involves executing 32 protocols and algorithms and producing specific output files. By using formal semantics, we create standardized, machine-readable definitions of these concepts, ensuring that terms like "bioinformatics analysis" are consistently understood and applied across different datasets and platforms. This consistency facilitates more accurate data interpretation and supports meaningful integration of biobank data into broader research initiatives (83). ● Interoperability: As defined in Chapter 1, interoperability refers to the ability of different systems, datasets, and tools to work together seamlessly, regardless of their origin or structure. Using semantic web technologies, this can be achieved through the use of standardized vocabularies and ontologies that provide a shared understanding of terms and relationships. For example, two biobanks may record the same type of data (e.g., blood samples or genetic variants) but use different terminologies. Semantic interoperability ensures that these datasets can be harmonized, allowing researchers to integrate and analyse data from multiple sources. By enabling meaningful data exchange, semantic interoperability supports advanced research and collaborative discovery in genomics, precision medicine, and other fields. 2.2.1.1 Resource description framework (RDF) As a member of the World Wide Web Consortium (W3C) family, RDF, created in 1999, is a standard model for data interchange on the web. It allows for the merging of data between different schemas, even when those schemas evolve over time (84). This makes it ideal for biobanking, where datasets are continuously updated with new discoveries and evolving research 33 needs. RDF organizes data using a simple structure called a triple (85) which consists of three components: Subject: The entity being described (e.g., a bio-specimen), Predicate: The type of relationship (e.g., "is donated by"), and Object: The entity related to the subject (e.g., the donor). For example, RDF can represent the relationship between a bio-specimen and its donor as: Bio-specimen (subject) → is donated by (predicate) → Donor (object). This triple-based structure allows RDF to effectively model and represent relationships in data, making it ideal for integrating diverse datasets. A crucial aspect of RDF is its reliance on Uniform Resource Identifiers (URIs) to uniquely identify resources such as individuals, classes, or properties. These URIs follow a standardized format and can serve as global identifiers, ensuring the unique and stable reference of resources across the web. This enables researchers to retrieve consistent and reliable information about a specific resource, regardless of its location. The advantage of using RDF for biobanking data Flexibility and Scalability of RDF RDF is particularly beneficial for biobanks because of its ability to handle complex, evolving and interconnected data without requiring complicated schemas. In traditional databases, adding new data types (e.g., a novel biomarker) or establishing new relationships (e.g., linking a genetic variant to clinical outcomes) often requires extensive database restructuring. RDF, however, 34 allows data to evolve dynamically without the need of major modifications. For example, consider a biobank storing data about target region enrichment in genomic studies, which is a technique used to isolate and analyze specific regions of DNA, often for identifying mutations related to certain diseases such as cancer. In this context, the biobank may store information about the target region enrichment process to track which specimens underwent this analysis and to link the results to other clinical or genetic data for comprehensive research. RDF enables this by structuring information into triples, where each data element is linked to another through a defined relationship. One such triple might represent the relationship between "target region enrichment" and the protocol used to carry it out, where "target region enrichment" is the subject, "executes" is the predicate, and "target region enrichment protocol" is the object. This way, the biobank can capture not just the data on the specimen but also the specific method used for analysis. Similarly, another triple can capture the outcome of the process, such as linking "target region enrichment" (subject) to "DNA processed specimen" (object) through the predicate "has specified output." These structured representations allow for clear, machine-readable data relationships that enhance integration and discovery. RDF vs. Traditional Relational Databases In relational databases, data is organized in tables based on a complex schema and is stored in relation to one another (86). This schema is built and queried using self-joins and foreign keys, which dictate the exact structure the data must follow (e.g., specific columns and relationships must be present). As data complexity increases, more joins between tables are required, leading to slower query performance (87). RDF, on the other hand, allows for the creation of a standardized vocabulary, making it easier to connect information without requiring complex 35 joins. This simplicity enables RDF to accommodate a wide range of data and provides flexibility for modifications, mappings, and querying. As biobanking data expands, RDF supports this evolution without the need for constant restructuring. Queries in RDF are also more flexible, concise, and intuitive compared to equivalent SQL queries, which often become complicated due to multiple joins (88). The role of identifiers in RDF for Biobanking A fundamental advantage of RDF is its reliance on URI’s to represent entities such as bio-specimens, datasets, and research protocols. These persistent identifiers ensure consistency across different datasets, allowing bio-specimens to be traced reliably over time, even as additional data is generated. For instance, each bio-specimen within a biobank is assigned a stable identifier that remains unchanged, regardless of updates to its attributes or additional processing steps. This consistency is essential for interoperability, as it enables seamless data integration across multiple biobanks, ensuring that researchers can reliably access and analyze linked data across diverse sources. By leveraging RDF's flexible structure, standardized identifiers, and ability to dynamically incorporate new relationships, biobanks can overcome many of the limitations of traditional databases. This enables more efficient data integration, retrieval, and long-term usability of biomedical data. 2.2.1.2 Web ontology language (OWL) OWL is a semantic web language designed to represent complex relationships between entities in a structured, machine-readable format (89). It extends the capabilities of RDF by introducing logical reasoning and classification, making it easier for computers to interpret and infer new 36 knowledge from existing data. OWL extends the capabilities of RDF and facilitates the inclusion of advanced class expressions often composed of logical operators (e.g., specimen [blood specimen ∪ processed specimen], where the union operator ∪ indicates a new class composed of processed blood specimen) and property restrictions (e.g., [sequencing, subClassOf, planned process ∩ has some target region specification] indicates that sequencing is a planned process that targets with at least one target region). Its ability to formally define relationships makes it particularly valuable in domains such as biobanking, where data from multiple sources must be integrated and interpreted consistently. The advantage of using OWL for biobanking data OWL enables the creation of structured ontologies that facilitate data integration and interoperability across different research domains. By explicitly defining the properties and relationships between biological samples, experimental methods, and derived data, OWL allows for standardized representation of biobank resources, ensuring that data from various sources can be consistently understood and queried. Types of Properties in OWL OWL distinguishes between two types of properties to define relationships between entities: 1. Object properties define relationships between two entities. For example, an object property could describe that a "sequencing assay" (subject) "has specified output" (predicate) as "sequencing data" (object). 'sequencing assay' and 'has specified output' some 'sequencing data’ 37 2. Data properties define relationships between an entity and a literal value. For example, a data property could indicate that a "specimen 001" (subject) "has storage duration" (predicate) of "29.5 months" (object). ‘specimen 001’' has storage duration “29.5”^^xsd:decimal These properties allow for detailed annotations of data, making it possible to describe biobank entities with precision. Through such properties, you can create extensive vocabularies to define concepts in a structured and consistent way (90). By leveraging OWL’s ability to express these relationships in a structured manner, biobanks can establish detailed, machine-interpretable representations of their data. This allows researchers to perform advanced queries, infer missing knowledge, and seamlessly link new data with existing biobank resources. 2.2.1.3 Description logic Description logic (DL) is defined as “knowledge representation languages for representing and reasoning about knowledge of an application domain in a structured and well-understood way.”(91). DL provides a formal syntax and semantics for expressing and reasoning about concepts, individuals, and relationships, making it particularly useful in a field such as domain knowledge engineering. DL is a foundation of many ontology languages, including OWL, which represents formal semantics (terminological knowledge base), and the provision of reasoning and inference systems (92,93). 38 The advantage of using DL for biobanking data Conceptual representation DL enables precise definition and classification of biobank-related concepts, ensuring clear differentiation between data entities. For instance, "DNA processed specimens" can be explicitly defined along with their associated properties, such as the method used for processing. This level of conceptual clarity is essential for data integration, retrieval, and cross-platform interoperability (94). Axioms and inference DL employs axioms—formal statements that define constraints and relationships between entities. These axioms ensure that data adheres to predefined rules and structures. For instance, the axiom “Each DNA processed specimen has exactly one specimen donor, and each specimen donor may donate multiple specimens” introduces cardinality constraints and defines the relationship between specimens and donors. This axiom ensures that each specimen is accurately linked to its donor while allowing for the possibility that donors may contribute more than one specimen. each “DNA processed specimen” has exactly one “specimen donor”, and each “specimen donor” may donate multiple specimens. By employing such axioms, DL helps maintain the integrity of biobanking data by governing relationships and properties. It also supports automated reasoning mechanisms, allowing biobank systems to infer new knowledge from existing datasets. For example, DL reasoning could automatically identify specimens associated with a particular donor across multiple studies or 39 flag inconsistencies in data relationships, streamlining data management and improving research outcomes. 2.2.1.4 SPARQL Protocol and RDF Query Language SPARQL Protocol and RDF Query Language (SPARQL) is a standardized query language used to retrieve and manipulate RDF-based data. It allows researchers to search across diverse datasets, identify relationships between entities, and retrieve structured information efficiently. Unlike traditional databases, SPARQL can query distributed and heterogeneous data sources, making it a powerful tool for biobanks managing vast amounts of structured and semi-structured biomedical data (95). One of SPARQL’s key advantages is its support for federated queries, which enable searches across multiple repositories (called endpoints) simultaneously. This capability, enabled by Hypertext Transfer Protocol (HTTP)-based transport, allows seamless access to distributed data sources without requiring data centralization (96). The advantages of using SPARQL for biobanking data Scalability and flexibility: Biobanks store large-scale, highly heterogeneous data, including specimens, clinical records, and in some cases specimen-derived data. However, researchers often struggle to determine which biobanks contain relevant data for their studies. Traditional data retrieval methods require manual searching or predefined database structures, limiting access and usability. SPARQL addresses this by enabling data federation, where researchers can query multiple biobanks simultaneously without having to copy or standardize the data in a 40 central repository. Because SPARQL queries operate on RDF-based structures, different data sources with varying formats can be harmonized on demand using predefined semantic relationships and ontologies (97). This approach enhances data integration and discovery, enabling researchers to efficiently locate relevant specimens or omics datasets across multiple institutions. Minimal technicality and redundancy: If an appropriate data synchronization method (the process of data and schema changes being propagated between the owners and query engines) is used, there are no requirements for ETL the data. Using ETL for data management can be a limiting factor, especially regarding flexibility and scalability. Because the procedure is done in batches rather than in real-time, it limits biobanking data availability and freshness. For example, if a specimen or dataset is withdrawn for any reason, the related data must be removed from the discovery platform. Furthermore, because data sources and transformation logic may change over time, the complexity and upkeep of the ETL process increase. Additionally, more expertise and resources are necessary to develop and perform the ETL process because more tools or programming languages are required. In contrast, SPARQL enables direct querying of live data without duplicating it or maintaining complex ETL pipelines. It is based on a declarative syntax that is more accessible to users familiar with structured query languages and can be reused across systems that expose similar data structures. 2.2.1.5 Ontologies In information science, an ontology serves as a formal representation and definition of the entities, properties, and relations between the entities that substantiate a specific domain (98,99). 41 Ontologies provide a shared vocabulary that enables data integration, interoperability, and meaningful querying across datasets. They are particularly valuable in biobanking, where data comes from multiple sources and requires standardized representation to facilitate discovery and reuse. Types of Ontologies Ontologies can be classified based on their scope, purpose, and level of abstraction. Some key types include: ● Domain ontology (100): A domain ontology defines the meaning of terms, phrases, and concepts within a specific field or domain. For example, a biobanking ontology would establish rules and data structures that describe bio-specimens, processing methods, and related metadata. Similarly, other fields such as biomedical investigations or food science would have their respective domain ontologies to manage specialized terminologies. ● Top-level ontology (101): A top-level ontology encompasses the most general concepts, categories, and relationships assumed to be common across all domains. It provides a high-level structure that can be applied universally, across any field of study, without diving into the specificities of a particular domain. ● Application ontology (102): This type of ontology is developed to meet the requirements of a specific application or use case. It typically imports parts of relevant domain ontologies and tailors them to address the unique needs of a particular project. For instance, in biobanking, an application ontology might focus on supporting specific research queries by integrating parts of genomic and clinical data ontologies. 42 Most ontologies used in biomedical research fall under the category of domain (100) or application ontologies (102). To manage and organize these ontologies effectively, initiatives such as the Open Biological and Biomedical Ontologies Foundry (OBO) have emerged. OBO coordinates the development of non-overlapping, interoperable domain ontologies that can be reused in research contexts, promoting consistency and collaboration across the field. However, importing parts of different domain ontologies into a single ontology can be challenging because each is built using distinct tools or styles, often relying on different upper ontologies (which categorize entities differently). Application ontologies help resolve this challenge by importing the necessary parts of domain ontologies to meet specific use case requirements. OBO and National Center for Biomedical Ontology (NCBO) BioPortal The OBO Foundry (103) is an international joint effort among developers to provide a library of accessible and reusable ontologies. It currently hosts over 240 ontologies, which are thoroughly reviewed and listed in its repository for use by the broader scientific community. In practice, these ontologies are regarded as high-quality resources, offering a solid foundation for building new systems or expanding existing ones. Similarly, the National Center for Biomedical Ontology (NCBO) BioPortal (104) is the largest ontology repository dedicated to biological and biomedical sciences. It offers access to a wide range of ontologies and provides services such as the NCBO Annotator, which helps researchers automatically tag their data with relevant ontology terms. This ensures that data is well-structured and easily discoverable, significantly improving data sharing and collaboration across research efforts. 43 2.2.1.6 Semantic reasoner A semantic reasoner is a software application designed to process OWL ontologies and RDF vocabularies, enabling it to infer relationships and derive new knowledge from the data. By doing so, it can validate the consistency of an ontology and identify new, implicit relationships that may not be immediately apparent in the original dataset (105). This allows them to categorize entities under ontological hierarchies and to identify contradictions. In addition to checking for contradictions, reasoners are capable of generating novel inferences by expanding on the relationships provided in the ontology. For instance, if an ontology defines that "DNA extracted from a bio-specimen" is associated with a specific "genomic analysis process," a reasoner could infer that the resulting "DNA processed specimen" should be associated with downstream data, such as "sequencing results" or "gene expression analysis." This capability is particularly powerful for biobanking because it allows automated systems to identify complex relationships between bio-specimens, procedures, and resulting data—saving researchers time and ensuring that data is connected and interpreted in meaningful ways. By categorizing entities under ontological hierarchies, reasoners can also assist in the classification of newly added data, ensuring that it fits within the existing framework of the ontology. For example, when new bio-specimens are added to a biobank, a reasoner can automatically assign them to relevant categories (e.g., "processed tissue" or "plasma sample") based on the defined relationships in the ontology. This automated classification reduces the burden of manual data curation and improves the efficiency of biobank data management. 44 In summary, reasoners enhance the power of ontologies by verifying their internal consistency, generating new insights, and automating the classification of data. In the biobanking domain, this translates to more robust, scalable, and interconnected datasets, which can be used to drive more efficient and insightful research across biomedical fields. 2.3 Current gaps In this section, I will discuss the current gaps associated with biobanking data sharing and discovery. Each subsection will start with an example query that tests the current state and its limitations. These queries are initially formulated as competency questions (explained in 3.2), intended to determine the ontology knowledge based on the answers that the ontology should be able to provide. Accordingly, in each section, I will propose the utilization of technical (semantic web technology) or non-technical measures required to implement an efficient information discovery, usage, and exchange methodology. 2.3.1 Need for persistent identifiers and an application ontology Example query: Search for disease x omics-derived data y available in repositories a, b, c with a mutation in gene z. Problem: Biobanking resources lookups requires efficient data discovery and integration across multiple repositories. However, current practices lack a standardized approach for referencing and linking data, leading to fragmented and inconsistent information. PIDs are essential for cataloging specimens, specimen-derived data, repositories, and contextual metadata in a searchable and structured format. Without them, researchers face difficulties in linking bio-45 specimens and associated datasets across different platforms. Furthermore, beyond the need for unique and persistent identifiers, an application ontology is needed for ensuring semantic interoperability and structured data integration. While PIDs uniquely identify entities, an application ontology defines relationships between them, enabling meaningful queries that span multiple domains. Repositories often function as isolated systems with locally assigned identifiers, making it difficult to track and connect data when these identifiers are not globally unique or standardized. For instance, two repositories may assign the same identifier to different entities, leading to misclassification and inconsistency in data integration. This lack of standardization complicates large-scale data discovery and hinders access to critical datasets. The use of PIDs has gained significant traction in recent years due to their importance in ensuring that biobank data can be effectively managed, discovered, and reused. One of the key frameworks guiding the proper handling of research data is the FAIR principles. These practices extend beyond just the data itself, applying also to the algorithms, tools, and workflows that generate or process the data (67) (106) (107). By associating PIDs with standardized metadata, researchers can ensure that data and its related resources adhere to FAIR guidelines, making them easier to locate, use, and integrate. To fully meet the FAIR criteria, each PID must be clearly linked to the corresponding entity, indexed in machine-readable formats, and accompanied by appropriate metadata fields. Figure 2.1 illustrates how PIDs contribute to fulfilling key components of the FAIR principles. The diagram presents an example of the term biobank (OBIB_0000616), which is uniquely identified using a PID. This PID serves as a global, 46 persistent link to the entity, ensuring its traceability across different platforms. It not only provides a stable reference for the biobank itself but also connects it to external resources, such as related entities and metadata records. By assigning PIDs to key entities, biobank data can be systematically cataloged, discovered, and reused across different research environments. This structured approach enables researchers and machines to efficiently retrieve relevant information, enhancing data findability and accessibility. Additionally, integrating metadata with PIDs ensures semantic interoperability, allowing related data and processes to be consistently referenced across repositories. Figure 2.1 An example of PID representing the “biobank.” A biobank record provides structured information about the entity itself, including a textual definition and closely related resources, such as the parent class of the entity. The PID: OBIB_0000616 supports the findability of data associated with biobanks. The associated URL (http://purl.obolibrary.org/obo/OBIB_0000616) can be accessed from any internet browser worldwide. Additionally, the use of standard vocabularies and links to other PIDs, such as those in other ontologies, enhances interoperability. The OBO Foundry’s commitment to citability, high-quality metadata, and up-to-date records fosters trust in these PIDs, ensuring data reusability across research projects and repositories. 47 While PIDs provide a standardized way to uniquely identify and reference bio-specimens and datasets across repositories, they do not inherently capture relationships between different entities or enable advanced querying across domains. To achieve semantic integration, a structured approach is required to define and connect these entities in a meaningful and interoperable way. Ontologies play a crucial role in this process by providing a structured framework for defining, organizing, and linking biobank-related concepts. Specifically, biomedical ontologies, such as those developed by the OBO Foundry, ensure that terms and concepts have unique, persistent identifiers that facilitate data discovery and retrieval across different repositories. However, individual ontologies alone are often insufficient to fully capture complex research queries. For example, a query like: Search for disease x omics-derived data y available in repositories a, b, c with a mutation in gene z requires terms from multiple domain ontologies, such as: • OBIB (for biobank-related terms) • OBI (for experimental methods and sequence data) • Disease Ontology (for diagnosis and disease classification) To enable seamless data integration and reasoning, an application ontology is needed to align these domain-specific ontologies under a unified schema. This structured alignment ensures semantic consistency, allowing researchers to retrieve and analyze data across multiple repositories without ambiguity. Furthermore, application ontologies selectively borrow terms from domain ontologies to meet specific research needs. Domain ontologies can be extensive, often containing thousands of terms covering a broad range of concepts. However, not all terms are necessary for addressing 48 specific research questions. To maintain agility and efficiency in software applications, only a relevant subset of terms is typically required for practical implementation. By carefully curating and integrating only the essential terms, application ontologies ensure that data remains structured, interoperable, and computationally accessible without unnecessary complexity. Table 2.1 illustrates examples of large-scale domain ontologies commonly used in biomedical research, along with the total number of terms they contain. This highlights the breadth of these ontologies and the necessity of selectively integrating only the terms relevant to specific applications, such as those required for this thesis. Relevant ontology Number of terms The ontology of Biomedical Investigation (OBI) 4989 Information Artifact Ontology (IAO) 263 Genomic Epidemiology Ontology (GenEpiO) 8727 Table 2.1 Examples of number of terms in domain ontologies This table presents examples of large-scale domain ontologies frequently used in biomedical research, along with the number of terms they contained. These ontologies represent distinct but complementary domains: the Ontology for Biomedical Investigations (OBI) covers experimental protocols and data types; the Information Artifact Ontology (IAO) models metadata and data representation artifacts; and the Genomic Epidemiology Ontology (GenEpiO) focuses on infectious disease genomics and epidemiological reporting. The wide range in term counts—ranging from a few hundred in IAO to several thousand in OBI and GenEpiO—illustrates the breadth and complexity of these domain ontologies. This underscores the importance of selective term integration in application ontologies, which borrow only the relevant terms needed to support specific research use cases. In the context of this thesis, such selective integration enables the construction of an agile, semantically consistent framework for querying and integrating biobank contextual data without overwhelming the system with unnecessary ontological overhead. 49 The current state and challenges are summarized as follows; I. Local identifiers: Data owners currently use local identifiers assigned within specific data sources. Different sources (e.g., biobanks, repositories, and public databases) are created independently. While these identifiers are unique within a single system, they lack global uniqueness, meaning that two disconnected systems can generate the same identifier for completely different entities. For example, “BR_0453” might denote a bio-specimen type in database A, but a sequencer device in database B. This can lead to confusion and errors in data integration. II. Standardized terminologies: Despite the prevalence of local identifiers, well-curated standardized terminologies such as SNOMED and the OBO Foundry ontologies—including domain-specific examples like OBIB—exist. These standardized systems assign globally unique, persistent identifiers to entities, which helps reduce the risk of confusion and misclassification across different repositories. III. Lack of contextual data standards in omics: Existing omics data standards, such as MIAME, lack formal guidelines for contextual data formats and persistent identifiers. MIAME outlines what information should be captured but does not provide a formal structure for representing this data. This has made it difficult to retrieve and reuse omics data, even though MIAME guidelines are widely adopted. For example, microarray data is often hard to locate or reuse due to inconsistent formatting and a lack of standardized identifiers (108). 50 IV. Linking reference vocabularies to interface terminologies: In practical applications, particularly in clinical settings, reference vocabularies must be linked to interface terminologies. Interface terminologies provide clinically relevant terms that are mapped to standardized vocabularies, allowing for easier data entry into systems (109). Reference vocabularies, such as SNOMED CT, are more concept-based and designed for broad interoperability, while interface terminologies simplify data entry. Historically, requiring healthcare professionals or researchers to input complex codes or technical terms into systems was impractical and time-consuming. As a result, the need for a "crosswalk" between user-friendly terms and standardized vocabularies became apparent (110). For example, Ontology for Biomedical Investigation (OBI) uses the label "target gene specification (directive information entity)" with the URI [http://purl.obolibrary.org/obo/OBI_0001962]. This term precisely defines a genomic region targeted by a planned process, such as an assay. However, clinicians rarely use this exact term in practice. Instead, they might say "target region" or "gene panel." Bridging the gap between formal ontology terms and the simpler, everyday clinical language is essential for consistent and accurate data entry. More examples of these non-standard terms are illustrated in Table 2.2, showing the different interface terminologies used in clinical reports from diagnostic service providers. 51 (A) Gene (Transcript) Nucleotide (Protein) Zygosity MAF Variant classification Disorder PKLR (NM_000298.5) c.1648G>A (p.Asp550Asn) Hom. na Likely pathogenic (Class 2) (Class 2) Pyruvate kinase deficiency (266200, AR) MTHFR (NM_005957.4) c.973C>T (p.Arg325Cys) Hom. 0.000025 Likely pathogenic (Class 2) Homocystinuria due to MTHFR deficiency (236250, AR) (B) GENE VARIANT COORDINATES AMINO ACID CHANGE SNP IDENTIFIER ZYGOSITY IN SILICO PARAMETERS CPLANE1 NM_023073.3:c.7778G>A p.(Trp2593) rs863225159 heterozygous PolyPhen: N/A Align-GVGD: N/A SIFT: N/A MutationTaster: Disease-causing Conservation_nt: weak Conservation_aa: (C) GENE VARIANT POSITION ZYGOSITY CLASSIFICATION BRCA1 c.181T>G p.Cys61Gly g.41258504A>C Heterozygous Pathogenic Table 2.2 Examples of different interface terminologies used in clinical reports from diagnostic service providers This table shows how different diagnostic service providers use varied interface terminologies when reporting genetic variants. These terms are designed to be easily understood by clinicians but often differ from the standardized representations, posing challenges for data integration and semantic interoperability. Example (A) presents a clinical report with fields such as gene name, transcript ID, nucleotide and protein changes, zygosity, minor allele frequency (MAF), variant classification, and associated disorder. While terms like “likely pathogenic” and “gene panel” are common in practice, they may not directly align with standardized data models without additional mapping. Example (B) includes variant coordinates, SNP IDs, zygosity, and in silico prediction results (e.g., PolyPhen, MutationTaster). Although informative, this format is optimized for human readability rather than structured analysis. Example (C) offers a streamlined summary of variant details, including genomic position, zygosity, and classification, using terminology that may differ across institutions. 52 Desired outcome: ● A controlled vocabulary that enables data harmonization and integration across data sources and domains, with standardized terms that are human–understandable, unique, self–explanatory. ● Non–ambiguous labels, and persistent identifiers. Requirements: 1. The need to import available vocabularies (from pre-existing ontologies) with persistent IDs into a single application ontology to enable information integration across data repositories and domains. 2. Request an appropriate persistent URL (PURL) from the OBO Foundry. This unique PURL within the OBO Foundry library allows the source of an entity or term (e.g., class, property) from any ontology to be identified immediately by the prefix of the identifier. Unlike a regular Uniform Resource Locator (URL), maintaining PIDs through well-established external archiving registries, such as OBO Foundry, keeps PIDs permanent and persistent. 3. For newly proposed terms (terms that do not already exist elsewhere), the need to curate standardized vocabularies with persistent IDs describing omics, contextual data, and related information and to integrate them into biobanking ontology. 53 2.3.2 Need for data granularity (What level of data can be accessed?) Biomedical research increasingly depends on data to answer research questions, which has spurred a growing interest in data discovery tools and methods (111,112). Data discovery refers to the exploration of datasets through visual tools that enable non-technical researchers to find the data they need from large datasets (113). It's important to distinguish data discovery from data access. While discovery involves identifying what data is available and where it resides, such as pinpointing which biobank holds specific data, it does not involve retrieving or viewing that data. Data access, on the other hand, entails the actual retrieval, viewing, and downloading of the data (114). The distinction between data discovery and access becomes evident when looking at the tools and methods used to store, query, and annotate datasets. For example, in molecular pathology labs, specific gene mutation information is often stored in PDF lab reports. This practice presents several challenges, including (i) privacy concerns related to patient-identifiable information, as illustrated in Supplementary Methods A.1 and A.2, and (ii) data extraction difficulties, since machines cannot directly access or query the content of PDF files. As a result, manual cleaning, curation, and standardization are required to make the data usable. A similar privacy risk arises with VCF data files, which are often used to store gene sequence variation information. While it might seem impossible to identify individuals using the content of a VCF file, studies have shown that it is possible to re-identify individuals in certain cases by querying as few as 250 single nucleotide polymorphisms (SNPs) (115). Even if direct re-54 identification is not possible, using direct-to-consumer genetic testing services could potentially reveal close or distant relatives, inching closer to identifying the individual. Both data granularity and aggregation are key aspects of data quality, influencing the data discovery and access processes. Data aggregation refers to combining data from different sources or levels to generate a summary or new perspective, while data granularity refers to the level of detail or precision within a dataset. Granular data allows for flexible aggregation or disaggregation based on the needs of different queries, making it easier to combine with other datasets and enhancing the ease of handling and querying the data. Example query: Is there a VCF file of omics-derived data for disease x with a mutation in gene y? Problem: Privacy concerns with omics data require aggregated access control. Data aggregation involves collecting data from multiple sources and summarizing it into a simplified, selected view. In biobanking, privacy must always be a top consideration. Unlike other data discovery methods, aggregation-based answers (such as yes/no) facilitate de-identification, in a way that sharing via VCF files does not (116) 55 Desired outcome: The solution should enable data discovery through restricted contextual data, using granular access based on rules and permissions. This approach would allow researchers to access data securely while minimizing the risk of exposing sensitive information, and at the same time, safeguarding institutions from inadvertently leaking private data to unauthorized individuals. For example, a researcher could be granted permissions to view only aggregated yes/no responses (e.g., whether or not certain data exist), while a registered researcher with higher privileges could access more detailed contextual data of a specimen or dataset. Requirements: 1. The need to develop a formal contextual data format and content for each experimental and computational entity that supports data discovery based on competency questions. Each contextual data entity should be independently discoverable through URI. 2. Comply with available laws and regulations at all stages, including sharing, discovering, and data re-use. 3. The view of contextual data must be restricted based on several dimensions, such as the user permission level, specific data variables, and restrictions concerning the period. Certain thresholds could prevent the re-identification of an individual. 4. The need to define the granularity of the resource of identification. In other words, it is essential when creating a new PID to define how much detail the data for the identifier covers, whether it covers only just properties of the resource, the full details of the resource, or even the resource plus any closely related entities. 56 2.3.3 Are we looking for data standards, minimum information requirements, or both? ISO defines a metadata standard as “a high-level document that establishes a common way of structuring and understanding data and includes principles and implementation issues for utilizing the standard” (117). The ISO standards do not specify how exactly to measure or describe something, and consequently, people who follow ISO standards still end up with datasets that aren't immediately comparable. Properly describing experimental contextual, which includes data is critical to accurately interpreting the data. An adequate description includes a (i) list of attributes recommended for the description of omics datasets, and (ii) a proper exchange format for the contextual data attributes. Several successful initiatives in the genomics research field have developed recommendations for the contextual data needed to describe specific biomedical assays. These recommendations have resulted in the creation of "minimum information requirements" that provide assay-specific guidelines. For example, MIxS (60), MIAME (61), MIAPE (63). Although these documents offer consensus on describing experimental-derived data, such as genomic sequences, "metadata standard" requirements still vary based on the scientists defining them. Given the rapid evolution of wet lab methodologies, computational analyses, and software, it is crucial to include detailed descriptions of the methods, tools, and instruments used. Without this contextual metadata, sequence data are of limited value and difficult to interpret. 57 Example Query: Search for any edits or modifications applied to the “specific raw data.” The problem: One of the major challenges in ensuring the findability and reusability of omics data is the lack of comprehensive and standardized metadata. While metadata is essential for describing contextual details such as sample collection, processing methods, and sequencing protocols, many critical attributes are either missing, inconsistently recorded, or stored in unstructured formats. This inconsistency creates barriers to data discovery, integration, and reuse. For example, quality control details of sequencing data, an essential component for downstream analysis, are often absent or inadequately documented. Similarly, variations in data preprocessing steps, sequencing protocols, and computational workflows are rarely captured in structured, machine-readable formats. Without these details, researchers face significant challenges when attempting to interpret raw sequencing data, assess its reliability, or integrate it with other datasets. Moreover, even when metadata is available, it is often not indexed in a searchable format, making it difficult to retrieve relevant datasets efficiently. This lack of structured contextual information severely hampers data discovery, integration, and reproducibility—fundamental requirements for modern data-driven biomedical research. Desired outcome: Proper standards of the information about omics contextual data, including sample metadata (e.g., sample type, sample size), as well as laboratory (e.g., collection method), clinical outcomes (e.g., recovery), epidemiological (e.g., age, vaccination status), and methods (e.g., sampling, sequencing, bioinformatics), as well as a proper mechanism for consistent structure and formatting. 58 Gap: There is a need for additional standards and suggested minimum information checklists that account for variations in research methodologies, computational analyses, and research questions. Current standards are insufficient for ensuring consistent data exchange and reuse across different contexts. 2.4 Discussion Digital information about different biobanking entities - specimens, devices, people, digital assets, organizations may be stored in varying formats, identifiers, annotations, schemas, logical descriptions, and modes of access. This variation increases the technical challenges related to data discovery and integration for biomedical researchers. The absence of scalable, well-structured infrastructures that can generate integrated content for biobanks will eventually result in minimal scalability, flexibility, and reusability, and the increased redundancy of data integration efforts among many research groups working on similar or related biomedical topics. In this chapter, I have presented my viewpoint on the role of semantic web technologies in creating scalable, intelligent infrastructures for biomedical data integration and semantic analysis on the Web. I have also identified several research gaps in current applications and literature, highlighting real-world queries for knowledge retrieval from biobanking sources and the limitations posed by current data structures. These challenges underscore the need for persistent identifiers and ontologies, the indexing of contextual data to enable appropriate data granularity, and the establishment of minimum information requirements to integrate omics data into biobanking ontologies. 59 I have further explored three primary challenges associated with these gaps: (i) data discovery, (ii) accessibility, and (iii) semantic heterogeneity, all of which hinder the effective reuse of biomedical data. Addressing these challenges is critical to advancing the utility of biobank resources and supporting broader biomedical research. 60 Chapter 3: Next Generation Biobanking Ontology (NGBO). 3.1 Author summary In previous chapters I have explored the role of biobanks in managing specimens and associated data (Chapter 1) and identified key challenges in integrating these resources (Chapter 2). This chapter introduces the Next Generation Biobanking Ontology (NGBO), an application ontology designed to harmonize, and standardize omics contextual data, enhancing data discovery, reusability, and integration across biobanking systems. NGBO address the inconsistencies in data representation, offering a solution to the challenges highlighted in previous chapters. NGBO is an open-source application ontology representing omics contextual data, licensed under the Creative Commons 4.0 license. The ontology focuses on capturing information about three main activities: wet bench analysis used to generate omics data, bioinformatics analysis used to process and interpret data, and data management. In this chapter, I demonstrated the use of the NGBO to add semantic statements to real-life use cases and query data previously stored in unstructured textual format. 61 3.2 Introduction The initial use of ontologies in biobanking began in 2014 with the introduction of the Ontologized MIABIS (OMIABIS) which is used in BBMRI infrastructure (81). OMIABIS was later extended and combined with Biobanking ontology, forming a novel Ontology for Biobanking (OBIB) (118). Ontologies, unlike databases that focus on storage and retrieval, allow for the integration of complex, interdisciplinary datasets by defining relationships between data elements. This approach enhances interoperability and the ability to connect data across systems. Researchers for example can effectively connect genetic variants in biobank datasets with functional annotations available in the Gene Ontology (GO) (119), as implemented in the Ensembl database (120). In this chapter, I present the Next Generation Biobanking Ontology (NGBO), an open biobanking ontology developed based on OBO Foundry Principles. NGBO has been evaluated and accepted by the OBO Foundry and is accessible through the Ontology Lookup Service (OLS) (121). NGBO uses the Saudi Human Genome Project (SHGP) as a proof-of-concept use case, demonstrating how semantic web technologies can improve querying and integrating biobank specimens and associated data. Another proof-of-concept use case involves grant-funded projects in Riyadh’s Second Health Cluster (R2), a network of healthcare hospitals and centers that also focuses on biomedical research. These projects generating diverse datasets with some bio-specimens deposited in the R2-associated biobank, dedicated to preserving and managing specimens for long-term research use. This biobank supports clinical and research activities by ensuring sample quality and accessibility, making it an ideal setting for demonstrating the capabilities of NGBO in managing and integrating biobank data. 62 3.3 Methods An overview of the NGBO development process—from scope definition to technical implementation and community feedback—is presented in Figure 3.1. This figure provides a high-level summary of the methodology described throughout this section. 3.3.1 Defining NGBO scope I approached the development of NGBO by first answering foundational questions about ontology’s purpose, scope, intended end-users, and usage; see Table 3.1, following the approach outlined by Suarez-Figueroa et al.(122). To define the scope and delimit the knowledge (i.e. specific data fields and relations) represented by NGBO, I initiated discussions with domain experts at Saudi Diagnostic Labs (SDL) and King Fahad Medical City (KFMC), both partners of the SHGP. These discussions involved researchers experienced in requesting specimen-derived data from repositories and helped us identify general scenarios. Based on these discussions, I clarified the need for NGBO and proposed several use-case scenarios. Following this, I formulated a set of Competency Questions (CQs) expressed in natural language to evaluate the scope and usage of the ontology, which was formalized into Description Logic (DL) queries according to the language used to represent the ontology. The CQs were used to verify the ontology’s requirements by ensuring that it could retrieve correct results, thereby validating its effectiveness and completeness in meeting the defined needs. The full list of CQs can be found in Supplementary Method B.1. 63 Figure 3.1 NGBO Ontology Development Workflow This figure summarizes the major steps taken in developing NGBO, including scope definition, data requirement analysis, specification development, ontology construction, and community alignment. It provides a high-level view of the methodology presented in detail in this section. 64 NGBO specification Purpose NGBO is developed to integrate contextual data, initially incorporating genomics and transcriptomics with existing biobanking ontologies. It aims to provide semantic services for biobank omics data management, discovery, and reuse. Scope This ontology includes descriptions of real-world physical objects such as specimens, computerized objects such as datasets, and planned processes and related specifications. Although bio-specimens are collected from various sources (e.g., humans, animals, plants, and the environment), the current NGBO scope focuses on human biobanks. The ontology can be extended to other species depending on use-case requirements. Intended end user User 1: Bioinformaticians and computational biologists who require integration and analysis of -omics data from various biobank specimens. User 2: Data curators and managers responsible for maintaining biobank databases and ensuring data quality and interoperability. User 3: Policy makers and regulatory bodies who need standardized and accessible contextual data about omics 65 dataset to inform health policies and regulations. User 4: Collaborating with research institutions and consortia that aim to federate biobank data across multiple repositories for large-scale studies. Intended use Use 1: Facilitate the discovery and retrieval of specific biobank specimens and associated -omics data through standardized contextual data integration. Use 2: Improve the quality and consistency of biobank data by providing standardized templates and guidelines for data curation. Use 3: Policy makers and regulators can ensure that biobank data meets regulatory standards and is uniformly reported. Use 4: Standardizing data representation, which is crucial for effective data federation. This will enable large-scale studies by combining data from various sources, increasing the statistical power and scope of research findings. Table 3.1 NGBO specifications This table summarizes NGBO’s purpose, scope, intended users, and primary use cases as defined during the early stages of development. These specifications guided ontology design to ensure alignment with real-world biobank data needs. They also informed the formulation of competency questions and use-case scenarios to validate the ontology's coverage and utility. 66 Ontology development process Next, based on defined scope and usage, I cataloged and studied relevant specifications and existing resources. This included reviewing a selection of public repository requirements, such as those from the National Center for Biotechnology Information (NCBI) BioSample, as well as requirements from the CAP laboratory guidelines, a particular attention was given to the CAP molecular pathology documentation requirements (123) to ensure NGBO supports essential quality assurance and traceability practices. This involved modeling processes related to both wet bench assays and bioinformatics pipelines. I also examined other available guidelines, such as the Minimum Information About Microarray Experiments (MIAME), the Minimum Information about any (x) sequences. The results of this analysis are presented in Table 3.2 Additionally, I considered relevant aspects of the American College for Medical Genetics and Genomics (ACMG) guidelines, such as variant classification guidelines, and clinical laboratory reports from King Fahad Medical City. However, this initial review did not provide sufficient data fields to fully address the NGBO use-case scenarios because the reviewed guidelines and reports were either too general or did not cover specific aspects of biobank data and omics integration and contextual data requirements unique to the NGBO framework. As a result, I needed to expand our data sources for a more comprehensive analysis. To address this gap, I screened public contextual data from a selection of journal publications from 2017 to 2019 by searching PubMed using a snowball sampling method. I began with relevant papers and selected additional ones based on their titles, abstracts, and references, continuing until I gathered enough data to address the NGBO specification questions. Keywords used in our search included “biobank data management,” “omics data integration,” “biobank guidelines,” “data standardization,” “minimum information requirements,” and “specimen metadata.”. The results of this keyword search can be found in 67 Supplementary Method B.2. From this expanded analysis, I identified a comprehensive list of contextual data required for accurately reporting the experimental procedures involved in wet bench and bioinformatics analysis. This list formed the foundation for constructing the minimum information checklist, which includes essential data fields for consistent and transparent reporting. The distinction here is that the comprehensive list encompasses all relevant contextual data, while the minimum information checklist specifically targets the key elements needed to ensure interoperability and standardization. 68 Requirements needed to address NGBO use-cases (NGBO usecases requires granular, computationally indexed data fields to ensure precise indexing and retrieval of biobank and omics-related data. This level of detail is essential for harmonizing diverse datasets and enabling automated processing, integration, and analysis across various systems.) NCBI BioSample College of American Pathologists documentation requirement Minimum information about a microarray experiment MIxS standard (Minimum Information about any (x) Sequence) Minimum information requirement (specific data fields) Standards Minimum information requirement (specific data fields) Standards Minimum information requirement (specific data fields) Standards Minimum information requirement (specific data fields) Standards Sample metadata (Covers data fields necessary for identifying and describing samples) ✔ ✔ ✔ Sample processing (Includes details on how samples are handled) ✔ ✔ ✔ Experimental design (Include data fields about the planning and structuring of experiments) ✔ ✔ ✔ ✔ ✔ Data sharing (include data sharing guidelines and highlights any deficiencies.) ✔ Quality control (Including data fields for procedures for ensuring data accuracy and integrity) ✔ ✔ ✔ ✔ 69 SOPs (Details on the standard procedures followed during biobanking and research) ✔ Result reporting (Including data fields about the documentation and communication of results) ✔ ✔ ✔ ✔ Detailed data processing details (Including data nformation on data processing methods) ✔ While NCBI BioSample offers essential sample metadata, it lacks detailed descriptions for omics data generation, bioinformatics pipelines, and quality control metrics. Additionally, it does not provide computational granularity for automated data indexing and integration, which is required by NGBO for enhanced interoperability The CAP checklist provides strong guidelines for specimen handling and lab processes but lacks coverage for omics data generation, including sequencing protocols, bioinformatics pipelines, and quality control measures. Additionally, CAP does not offer computational indexing capabilities, which are critical for NGBO's requirements to enable automated data integration and analysis across systems. The MIAME checklist focuses on microarray-specific data but lacks detailed fields for omics data generation, bioinformatics pipelines, and sequencing technologies. It also does not provide computational indexing capabilities, limiting its use for NGBO’s requirements for automating data integration and ensuring interoperability across systems. While the MIxS standard focuses on sequencing-related metadata, particularly sample conditions and environmental data, it lacks detailed fields for integrating biobank specimen data, omics data generation, and bioinformatics pipelines. Table 3.2 Comparison of NGBO use-case requirements with selected community standards and documentation guidelines This table presents an overview of how existing specifications—including NCBI BioSample, CAP documentation requirements (with emphasis on molecular pathology), MIAME, and MIxS—align with the data fields required to support NGBO’s granularity, traceability, and computational indexing. It highlights gaps where current standards fall short of addressing the integration and contextualization needs unique to biobank and omics data in the NGBO framework.70 3.3.2 Technical development To manage the development of NGBO, I set up a GitHub repository using the Ontology Development Kit (ODK). ODK is a toolkit designed for creating, maintaining, and standardizing biomedical ontologies, offering standardized, customizable, and automatically executable workflows for ontology lifecycle management (124,125). I used the Protégé ontology editor to build the ontology in the OWL format (89). To integrate external ontologies and guide term relationships, I adhered to OBO Foundry Principles (103). Basic Formal Ontology (BFO) (126) was utilized as the top-level ontology for NGBO development . To avoid redundant representations, I carefully selected and imported pre-existing terms from well-maintained ontologies. These ontologies are annotated with proper Permanent URLs (PURL)s, labels, and textual and logical definitions. For example, I imported terms from the Ontology for Biomedical Investigations (OBI) to describe entities involved in biomedical research, including material entities, processes, and protocols, using OWL import (127). Additionally, from OBIB (118), I imported terms to describe specimens, donors, and specimen management (118). Information Artifact Ontology (IAO) (128) is another relevant ontology used as a mid-level ontology and includes terms describing identifiers, software solutions, and documents. In total, terms from twenty ontologies were imported. The top five ontologies by term count are CL (660 terms), OBI (659 terms), OBIB (167 terms), IAO (70 terms), and RO (68 terms), collectively accounting for 83.3% of all imported terms. A full breakdown is presented in Figure 3.2. 71 Figure 3.2 Set of ontology namespaces used in NGBO This figure presents the distribution of ontology namespaces from which terms were imported into NGBO. To promote reuse and avoid redundancy, terms were selectively integrated from well-established, OBO-compliant ontologies using OWL import mechanisms. The figure highlights the top contributing ontologies, including the Cell Ontology (CL), Ontology for Biomedical Investigations (OBI), Ontology for Biobanking (OBIB), Information Artifact Ontology (IAO), and the Relations Ontology (RO), which together account for 83.3% of all imported terms. These ontologies cover a wide range of domains such as cell types, biomedical protocols, specimen handling, information artifacts, and semantic relationships. This reuse strategy ensures semantic consistency, alignment with community standards, and facilitates interoperability across biomedical data systems. As part of NGBO’s technical design, I implemented features for specimen tracking and traceability by modeling key process relationships (e.g., has performer, has specified input/output, part of) and introducing object properties such as trace from and track to. These relations support bidirectional tracing of specimen and data flow across experimental and computational processes, fulfilling traceability requirements from standards like CAP. I also introduced basic modeling of data privacy and access control, using semantic roles (e.g., authorized user, data owner) and object properties like accessed by to demonstrate how NGBO could support controlled data access within federated biobank environments. 72 Input from the broader OBO Foundry community was solicited during the International Conference on Biomedical Ontologies (ICBO) 2019, where NGBO was first presented. The feedback from this conference played a critical role in refining NGBO’s scope and alignment with existing ontologies. Additionally, NGBO benefited from sustained discussions with the OBIB development group over multiple months, particularly regarding the modeling of specific terms and the integration of OBIB concepts within NGBO. These discussions facilitated the refinement of NGBO’s structure and ensured that NGBO-defined terms align with existing ontologies within the OBO framework. As part of this alignment, there was a consensus that NGBO, as an application ontology, would contribute newly defined terms to relevant domain ontologies, reinforcing its interoperability within broader biobanking and biomedical ontology ecosystems. The OBIB group was generally supportive of NGBO’s approach, particularly in its efforts to enhance semantic integration across biobank data resources. 3.4 Results NGBO is an open-source application ontology written in OWL and licensed under the Creative Commons Attribution 4.0 International Public License. It is deposited in a public GitHub repository, where ontology releases and term requests are maintained, and accessed by OLS at https://www.ebi.ac.uk/ols4/ontologies/ngbo. The latest release, published on September 10th, 2024, contains 1,515 classes, 252 properties, 165 NGBO-defined classes, and 1,942 logical axioms and relations. The review of existing standards revealed significant gaps in satisfying NGBO use-cases. Table 3.2 provides a comprehensive overview of how various standards align with NGBO, highlighting 73 specific gaps and overlaps. Biobanking and specimen handling standards often lack the granularity needed to capture details on sequencing platform versions, quality control metrics, and bioinformatics pipelines—critical for omics data integration. Conversely, while sequencing standards like MIxS capture these details, they do not address specimen handling or consent information, creating challenges for automated data integration. 3.4.1 Ontologizing contextual data structure for wet bench analysis Given the rapid evolution of sequencing technologies, ensuring traceability for each processing step is crucial (129). NGBO defines an omics data generation assay as “a planned process that generates omics data through a wet bench process, such as sequencing techniques.” Each NGBO-defined entity related to omics data generation assays includes multiple participants and concepts, as outlined in the suggested information checklist for omics data generation assays, see Table 3.4. Each assay specifies its inputs and outputs to achieve its objectives. For instance, a “DNA processed specimen”—defined as “a processed specimen which is the output of preparing a DNA sample for further analysis”—is captured as an input of the process using the “has specified input” relation. Similarly, the measurement datum “output data file”—defined as “A data file which contains the output of data transformation process”—is captured as an output using the “has specified output” relation. Figure 3.3 (A) shows how NGBO integrates and manages information describing the wet bench process. 3.4.2 Ontologizing contextual data structure for dry bench analysis. Other NGBO development efforts focus on concept curation for bioinformatics analysis and software. NGBO captures details such as process participants, specified inputs and outputs, and 74 execution information (including executor name, date, and run Identifier (ID)) for bioinformatics processes. It also incorporates plan specifications, including algorithms, protocols, and bioinformatics software. For instance, "bioinformatics analysis software" refers to software designed to extract meaningful information from molecular biology data. I propose a suggested minimum information checklist for bioinformatics software and Single Nucleotide Polymorphism (SNP) reporting, detailed in Tables 3.5 and 3.6. NGBO models bioinformatics software components, including algorithms, databases, and protocols, and links these to wet lab processes. This connection enables bioinformatics analysis of specimen-derived data while maintaining traceability to the original specimen. Version traceability of bioinformatics analysis software is recorded using a "version number" datum and the "is about" relation 'version number' and 'is about' some bioinformatics analysis software For example, NGBO captures command-line parameters, such as the “-A INT” flag used in the Burrows–Wheeler Aligner algorithm, which is crucial for data re-analysis. Additionally, NGBO augments data management activities, including de-identification, storage, and transfer, with detailed information on storage data and protocols. This comprehensive approach ensures robust management and integration of bioinformatics data as illustrated in Figure 3.3(B), enhancing reproducibility and data handling efficiency. Supplementary Method B.3 includes representative examples of the contextual data terms ontologized within NGBO to support both wet and dry bench workflows 75 Figure 3.3 (A) Illustration of the omics data generation assay. (B) Illustration of the bioinformatics analysis and a selection of possible pipeline components This figure also demonstrates how omics data generation is directly connected to the bioinformatics analysis workflow. Specifically, the measurement datum produced as the output of the data generation assay serves as the input for subsequent computational analysis. This highlights the continuity and integration between laboratory-based data production and downstream bioinformatics processing. 76 Data item (interface label) Ontology label Definition Guidance Example of use Sequence run executer name Name of data generation assay executor (NGBO) Name of personnel who executed the sequencing run Enter the name of personnel who executed the sequencing run Dalia Alghamdi Sequence run executer name Sequencing run date (GenEpio) Date the sequencing run was performed Enter the date the sequencing run was performed. The required granularity includes year, month, and day. 21/05/2022 Sequencing run execution note Assay execution note (NGBO) A textual entity of relevant notes or instructions regarding data generation assay execution Enter free text of relevant notes or instructions regarding your sequence run. Do not enter any information describing entities mentioned as part of this table (e.g. name of data generation assay executor or target gene specification) 77 Any protocols used in data generation assay Protocol (OBI) A plan specification that has sufficient level of detail and quantitative information to communicate between investigation agents, so that different investigation agents will reliably and independently be able to reproduce the process Select value from the pick list. If not available, request a new term by creating a new issue here: https://github.com/Dalalghamdi/NGBO/issues Target enrichment protocol (NGBO) Adapter ligation protocol (NGBO) Information about sequencing target region Target gene specification (OBI) A directive information specifying a coding genomic region, which is the focus of a planned process such as an assay in an environmental gene survey Enter gene panel name, users must clarify whether the gene panel is an in-house gene panel or public gene panel. Autism gene panel Assay kits used in data generation assay Assay kit name (NGBO) Processed material that contains a set of articles or equipment needed for a specific purpose Select value from the pick list. If not available, request a new term by creating a new issue here: https://github.com/Dalalghamdi/NGBO/issues QIAseq Human Exome Kit Versions of assay kits Assay kit version (NGBO) Sequencing kit version (GenEpio) Version of assay kit Select value from the pick list. If not available, request a new term by creating a new issue here: https://github.com/Dalalghamdi/NGBO/issues User entry: 1.0 78 Any devices used in data generation assay Device (OBI) A material entity that is designed to perform a function in a scientific investigation, but is not a reagent Select value from the pick list. If not available, request a new term by creating a new issue here: https://github.com/Dalalghamdi/NGBO/issues Illumina HiSeq 4000 Specimen identifiers Identifier (ISO) An identifier is a label that specifically refers to (identifies) an entity (instance/type). Enter identifier of processed specimen, which is the output of preparing a DNA sample for further analysis Specimen28728 Omics data generation assay output data file Data file name (NGBO) Data file generated by Omics data generation assay using a measurement device Enter data file name Read27828.fasta output data file format data file format (NGBO) Data file format is a standard means of encoding information is encoded for storage in a computer file Select value from the pick list. If not available, request a new term by creating a new issue here: https://github.com/Dalalghamdi/NGBO/issues FASTA Table 3.3 suggested information checklist for omics data generation assays This table presents a proposed checklist of essential contextual data elements for documenting omics data generation assays within biobank workflows. It includes commonly used labels alongside their corresponding ontology-aligned labels, definitions, guidance for data entry, and real-world examples. The checklist was developed based on NGBO specifications and includes terms imported from multiple ontologies including NGBO, OBI, and GenEpiO.79 3.4.3 Implementation of CAP Requirements in NGBO NGBO was developed to comply with CAP standards for omics data generation assays and biobanking data management. It incorporates detailed specifications to ensure that biobanks can meet CAP’s molecular pathology documentation requirements as published in (Aziz et al. 2015) for quality assurance, validation, and traceability. Here are key examples of how NGBO supports CAP compliance: ● Wet bench process documentation: NGBO models critical elements such as sample preparation, library preparation, and sequencing, linking them to specific reagents, instruments, and software used. This ensures each step is documented, meeting CAP’s requirement for detailed SOPs and facilitating the traceability of processes. ● NGBO provides a framework for documenting activities, capturing the entire workflow from specimen handling to data processing. This traceability is achieved through properties like “has specified input” and “has specified output,” allowing labs to maintain comprehensive records of all processes, as required by CAP. Later in the chapter, the Specimen Tracking and Traceability section provides a more detailed explanation of how NGBO supports comprehensive specimen traceability, using process properties like “has specified input” and “has specified output” to document every step and interaction in the specimen's lifecycle. ● Bioinformatics pipeline documentation and validation: NGBO models terms for tracking and documenting each step of the bioinformatics pipeline, including sequence alignment and variant calling. This supports CAP’s requirements for bioinformatics validation and version control, ensuring that all software components and configurations are traceable. 80 Data item (interface label) Ontology label Definition Guidance Example of use Bioinformatics analysis execution date bioinformatics analysis execution date (NGBO) Date on which analysis was performed Enter the date the bioinformatics analysis was performed. The required granularity includes year, month, and day. 21/05/2022 Bioinformatics software executor name name of bioinformatics analysis executor (NGBO) Name of personnel who executed the analysis. Enter the name of personnel who executed the bioinformatics analysis (text). Dalia Alghamdi software run ID software run identifier (NGBO) An identifier that denotes the unique run ID of the code executed Enter software run ID PID: 346524 Bioinformatics analysis execution note assay execution note (NGBO) A textual entity of relevant notes or instructions regarding bioinformatics analysis execution. Enter free text of relevant notes or instructions regarding your sequence run. Do not enter any information describing entities mentioned as part of this table (e.g., software run ID, bioinformatics software version number) This analysis did NOT identify genetic variants that may be responsible for other diseases unrelated to this individual’s clinical presentation. 81 Bioinformatics software name software name (NGBO) A name of the bioinformatics software utilized. Select value from the pick list. If not available, request a new term by creating a new issue here: https://github.com/Dalalghamdi/NGBO/issues 1) JWES pipeline. 2) CLC Genomics Workbench. Bioinformatics software version number software version number (NGBO) A version number of bioinformatics analysis software. Enter software version number, if the software/pipeline is an in-house product, it is recommended to follow a numbering system i.e., 3.01 or date-based following ISO-8601, i.e. date format YYYY-MM-DD v. 3.01 Bioinformatics software description software description (NGBO) A description of the bioinformatics bioinformatics software. Do not enter any information describing entities mentioned as part of this table (e.g. software run ID, bioinformatics software version number) QIAGEN CLC Main Workbench is used by tens of thousands of researchers all over the world for DNA, RNA, and protein sequence data analysis.. Bioinformatics software documentation URI software documentation URI (NGBO) A URI of associated manuals, documents, or guidance Enter a URI for a written text or illustration that accompanies computer software or is embedded in the source code https://github.com/drzeeshanahmed/JWES-Variant 82 Input data file name Data file name (NGBO) Data file generated by Omics data generation assay using a measurement device Enter data file name Read27828.fasta Input data file path data file URL (NGBO) The URL at which the data file is stored. Enter data file URL e.g. \\server01\kfmc\path Data file format data file format (NGBO) Data file format is a standard way that information is encoded for storage in a computer file Select value from the pick list. If not available, request a new term by creating a new issue here: https://github.com/Dalalghamdi/NGBO/issues FASTA Reference genome download URI reference genome URI (NGBO) A reference genome datum that represents a location where reference genome can be accessed online Enter reference genome download URI https://www.ncbi.nlm.nih.gov/genome/guide/human/ Reference genome build reference genome identifier (GenEpio) Identifier for a reference genome (used to build or compare an assembly) that is used in the genome browsers and in the community Enter reference genome symbol. GRCh38 83 Bioinformatics software parameter name software parameter name (NGBO) Name of a software option Enter bioinformatics software parameter name, do not add any extra characters. -q Bioinformatics software parameter value software parameter value (NGBO) Value to be fed into software option Enter bioinformatics software parameter value, do not add any extra characters. INT Command-line flag command line flag (NGBO) A symbol that is used to specify options for command-line programs. If applicable, enter any Command-line flag used. Appropriate command-line flags for running bioinformatics pipeline, for example, bwa - Burrows-–Wheeler Alignment l Bioinformatics analysis output data file URI data file URL (NGBO) A URL at which the data file is stored. Enter data file URL e.g., \\server01\kfmc\path Table 3.4 Suggested minimum information checklist for Bioinformatics software. This table provides a checklist of contextual data elements recommended for documenting bioinformatics analyses conducted on omics data. Each row outlines a commonly used interface data item, its aligned ontology label as defined in NGBO or relevant external ontologies (e.g., GenEpiO), a clear definition, data entry guidance, and an example of use. 84 Data item (interface label) Ontology label Definition Guidance Example of use Detected gene mutation detected gene mutation (NGBO) A conclusion based on data about a permanent change in the DNA sequence that makes up a gene (This entity is only used for data structuring purposes) Select value from the pick list. If not available, request a new term by creating a new issue here: https://github.com/Dalalghamdi/NGBO/issues KRAS mutation Gene names by HUGO Gene Nomenclature Committee (HGNC) HUGO gene nomenclature committee gene name (NGBO) The biological database by HGNC contains approved unique symbols and names for human loci, including protein-coding genes, RNA genes, and pseudogenes. HUGO Gene Nomenclature allows clear scientific communication. Select value from the pick list. If not available, request a new term by creating a new issue here: https://github.com/Dalalghamdi/NGBO/issues MTHFR Genomic coordinates Reference genome coordinate (NGBO) Reference genomic coordinate is a data item that describes the start and end position of a gene (or genetic element) on a chromosome. Enter genomic coordinates, and include the chromosome name, start, and end positions in the following format: chr1:1234570-1234870 chr1:1234678-1234567. RefSeq accession (transcript RefSeq accession identifier RefSeq accession is a data item describing the set of character Enter RefSeq record, a distinct accession number format that begins nm_00739.1 85 reference number) (GenEpio) sequences of a RefSeq database entry. with two characters followed by an underscore (e.g., NP_) Reference genome name and version reference genome version number (NGBO) reference genome that represents reference genome edition Select value from the pick list. If not available, request a new term by creating a new issue here: https://github.com/Dalalghamdi/NGBO/issues GRCh37 Coding information nucleic acid substitution (NGBO) A data item about the biological process in which one nucleic acid is replaced by a different nucleic acid. Enter replacement information of one nucleotide in a DNA with a different nucleotide in the following format: c.973C>T c.973C>T Variant allele frequency (VAF) allele frequency (STATO) http://purl.obolibrary.org/obo/STATO_0000254 Variant allele frequency is a data item that describes the percentage of sequence reads observed matching a specific DNA variant divided by the overall coverage at that locus. VAF determines somatic or germline mutations. Enter the percentage of sequence reads observed matching a specific DNA. Usually shown as a percentage (%) 60% Amino acid replacement data item amino acid substitution (NGBO) A data item about the biological process in which one amino acid is replaced by a different amino acid. Enter replacement information of one amino acid in a protein with a different amino acid in the following format: p. Trp26Cys p.Arg325Cys Table 3.5 Suggested minimum information checklist for SNP reporting This table outlines a structured contextual data elements for reporting of single nucleotide polymorphism (SNP) findings.86 3.4.4 NGBO evaluation 3.4.4.1 NGBO evaluation by OBO Foundry NGBO has been evaluated and accepted by the OBO Foundry after undergoing both automated checks and manual review by the OBO Foundry Operational Committee. Detailed discussion can be found in the OBO Foundry GitHub issue: https://github.com/OBOFoundry/OBOFoundry.github.io/issues/1819#issuecomment-1396905637. The evaluation included the ontology's title, ID space, repository location, contact information, issue tracker, license (CC-BY), and related OBO Foundry ontologies. NGBO adheres to key principles such as open access, unique URI, common format, and proper versioning. After its acceptance, NGBO was registered in the official OBO Foundry metadata registry, available at: https://github.com/OBOFoundry/OBOFoundry.github.io/blob/master/ontology/ngbo.md. For detailed compliance with each principle, see Table 3.6 OBO Foundry Principle How NGBO Meets the Principle Open NGBO is freely available and licensed under CC BY 4.0 on GitHub and OLS. Common Format NGBO is available in a formal language (XML/rdf and OWL) in concrete syntax. URI/ID Space NGBO has a unique IRI in the form of an OBO Foundry PURL: http://purl.obolibrary.org/ngbo. Versioning The latest version of the ontology is on GitHub, with previous versions also available on GitHub. Scope NGBO includes descriptions of real-world physical objects like specimens, computerized objects like datasets, and processes and specifications. Textual Definitions All NGBO classes have textual definitions. Relations Relations are reused from the Relations Ontology (RO), except for four specific relations defined by NGBO. 87 Documented Plurality of Users End-users include biobanking organizations, data collectors, omics researchers, and decision-makers. Commitment To Collaboration New terms introduced by NGBO will be submitted to relevant OBO Foundry domain ontologies when possible. The community can request edits/additions via GitHub issues. Locus of Authority Primary contact is Dalia Alghamdi, who manages communication between the community and NGBO developers. Maintenance The ontology reflects ongoing changes in scientific consensus and remains updated over time. Table 3.6 NGBO Compliance with OBO Foundry Principles This table summarizes how the Next-Generation Biobanking Ontology (NGBO) meets the core principles required for acceptance into the OBO Foundry. These principles ensure that participating ontologies are open, interoperable, and maintained according to community standards. 3.4.4.2 NGBO evaluation by CQs and reasoners To validate and test the adequacy of the ontology, I referred to the method used in a previous study (81). DL queries over an OWL file populated with MoC data instances and false positive and negative entries at the instance level. The MoC data were imported into the NGBO OWL file using the Celfie plugin (130). The DL queries are implementations of the previously proposed CQs. When evaluating the quality of NGBO, I considered two principles: 1. Clarity: NGBO does not create any misinterpretation during query execution or reasoning. 2. Consistency: all individuals were accurately categorized when the reasoning procedure was completed. Reasoning is deriving implicit facts from a set of given explicit facts. In NGBO, these facts are expressed in OWL and stored in Resource Description Framework (RDF) triplestores. For example, the following fact: “A bioinformatics analysis executes an algorithm” can be expressed in an ontology at the class level as “ ‘bioinformatics analysis’ [NGBO:6000122] executes some algorithm [IAO:0000064]“, while an instance of this fact involving a particular job run and 88 software reference: “bioinformatics analysis X123 executes algorithm Y456” can be stored in a triplestore. A reasoner is a software application that uses an ontology to infer relationships that should hold between existing classes and their instances. A reasoner categorizes entities under ontological hierarchies and identifies contradictions. To validate whether or not NGBO is consistent, I used HermiT (131) and Pellet (132) reasoners. Both reasoners confirmed the ontology's consistency, with no logical contradictions detected in the ontology's structure or axioms. To evaluate NGBO's ability to answer the competency questions (CQs), I ran description logic queries, listed B1 Supplementary method, after reasoning. The results confirmed that NGBO successfully retrieved the expected outputs for all CQs, demonstrating that the ontology effectively meets its defined requirements for querying biobank data. 3.4.5 NGBO use cases 3.4.5.1 Enhancing data integration and discoverability in SHGP using NGBO The Saudi Human Genome Project (SHGP), launched in 2014, aimed to create a comprehensive genetic database for the Saudi population. However, like many large-scale genomic initiatives, SHGP faced significant challenges in managing and integrating its diverse data types. Data generated from various bioinformatics tools, laboratory SOPs, and sequencing pipelines were stored in siloed systems, making discovery and reuse difficult. NGBO addresses the challenge of integrating diverse data types by introducing a structured, ontology-based framework. For example, each entity is assigned a globally unique identifier, such as 'DNA processed specimen' (NGBO_6000038). This specimen is linked to a 'sequencing assay' (OBI_0600047), which captures critical contextual data about the processing, including the specified input (e.g., Specimen 006), the output 'sequence data' (OBI_0000973), and the 89 performer (e.g., Illumina HiSeq platform). These relationships, defined using ontology-based fields like 'has specified input' and 'has specified output,' ensure that each component in the biobank dataset is contextually connected. Figure 3.4 illustrates how NGBO enables comprehensive linkage among data entities, thereby supporting efficient data governance and fostering the reuse of genetic resources. To evaluate NGBO’s impact, I manually assessed metadata terms used across SHGP sequencing submissions. Before annotation, 100% of entries documented library preparation and quality control information as unstructured free-text, making it difficult to retrieve or compare records across datasets. After applying NGBO, platform names were harmonized under a single controlled term, and both library preparation and quality control metadata were restructured using standardized ontology fields. This standardization enabled precise querying using DL queries introduced earlier in the manuscript by specimen type, platform, and processing protocol. By employing this level of granularity and precision, NGBO enables researchers to perform targeted queries across datasets, locating data such as nucleotide changes and variant annotations tied to specific assays or specimen types. This structured approach not only aligns with the FAIR data principles, but also enhances resource utilization by facilitating seamless data discovery and integration across biorepository platforms. To further clarify these results, NGBO’s structured framework has transformed SHGP’s data discovery workflow, replacing manual searches with ontology-powered querying and enhancing metadata consistency across biobank records. Previously, there were numerous inconsistencies in specimen metadata, such as variations in terminologies used for sequencing platforms (e.g., 'HiSeq 2500' vs. 'Illumina HiSeq'), and missing quality control details in sequencing metadata. By enforcing standardized terminology and structured metadata representation, NGBO has significantly improved data integration, 90 ensuring that sequencing data can be efficiently retrieved based on specimen and assay relationships. NGBO’s contributions to semantic interoperability and query automation are explicitly highlighted throughout the manuscript. Figure 3.4 Illustration of the approach implemented using NGBO to improve the discovery of SHGP data and the FAIRness of the data This figure illustrates how NGBO links entities such as processed specimens, sequencing assays, and resulting data using ontology-based relationships (e.g., has specified input/output). By assigning unique identifiers and standardizing metadata, NGBO enhances data integration and enables precise querying across SHGP datasets. 91 3.4.6 Application of NGBO for managing datasets generated in grant-funded projects in R2 The R2 Health Cluster, which consists of 64 hospitals and research centers, generates diverse clinical and molecular datasets, including studies that generate omics data, with some study samples deposited into the cluster’s biobank. However, each institution often uses varying formats and terminologies to describe similar data types, such as genetic mutations, sample handling, and sequencing protocols, complicating data merging and analysis. NGBO addresses these issues by standardizing study designs, interventions, and data types, ensuring consistent descriptions across projects. This standardization supports better data integration, discovery, and reuse, while also improving traceability of data and samples, ensuring transparency and reproducibility across the cluster. For instance, when expression Quantitative Trait Loci (eQTL) data is submitted to the R2 data governance team, NGBO assigns predefined identifiers to crucial data fields such as Chromosome Number (NGBO_6000476), Detected Gene Mutation (NGBO_6000439), and Expression Quantitative Trait Loci Data (NGBO_6000477). These identifiers ensure uniform representation for essential eQTL data fields while allowing partnering institutions to use different labels. NGBO also standardizes contextual data files for gene and SNP locations, which include information such as chromosome number, starting and ending loci, and SNP positions. Consistent SNP information is essential for accurate eQTL data analysis, as it enables the linkage between genetic variants and gene expression changes. To date, NGBO has been used to standardize metadata for 38 grant-funded datasets submitted to 92 the R2 Health Cluster. In addition to harmonizing metadata across bioinformatics and library preparation workflows, NGBO specifically addressed eight major SNP-related terms that required harmonization; these terms presented previously in Table 3.5. Prior to NGBO implementation, each dataset contained 5–9 inconsistencies in core fields such as SNP identifiers, variant representation formats, and gene symbols. After standardization using NGBO-defined terms and structure, all submissions aligned with cluster-wide metadata guidelines, and duplicate variant records due to inconsistent labels were fully resolved. This has significantly improved the efficiency of cross-study comparisons and enabled automated querying across research-generated datasets, with internal data governance committee feedback indicating a noticeable reduction in manual curation efforts. 3.5 Additional features: 3.5.1 Specimen tracking and traceability The detailed documentation of all specimen processing activities is critical for many reasons, including accreditation and regulatory body requirements, collaboration and data reproducibility, and audibility. For example, CAP requires traceability of all standard operating protocols for DNA/RNA specimen preparation, library preparation, sequence generation, and bioinformatics analysis. NGBO includes terms describing specimen processing, material entities, and information entities, including information about designated team members or personnel who perform any specimen- or data-related activities. To support end-to-end traceability, NGBO defines a set of object properties (relations) that describe how data and specimens flow through a series of processes. Central to this structure are the relations “trace from” and its inverse, “track to,” which enable bidirectional tracing of 93 specimens and associated data. The relation “trace from” includes several sub-properties that represent specific aspects of workflow provenance. For example, “has performer” denotes a relation between a planned process and a continuant—such as a person, organization, or device—that performs the process. “has specified input” links a process to the material entity that serves as its input, while “has specified output” connects it to the entity it produces. Both are inverses of standard OWL properties and help capture input-output dependencies within processes. The relation “part of” is used to represent compositional relationships, such as a subprocess being part of a larger experimental protocol. Conversely, the relation “track to” and its sub-properties serve as inverse properties of “trace from” and enable top-down exploration of how a dataset or output originated. When instances of processes, their inputs, outputs, and performers are explicitly represented, NGBO allows for ontology-based reasoning to infer and navigate complete chains of evidence. This facilitates bottom-up or top-down traceability using languages such as Semantic Query-Enhanced Web Rule Language (SQWRL). An example of this reasoning-based tracing approach is shown in Figure 3.5. 94 Figure 3.5 Using NGBO for specimen tracking and traceability (A) An example of relations between individuals. (B) A top-down tracing SQWRL query using the “trace_from” relationship. (C) Table of query results. 95 3.5.2 Data privacy and user access control When dealing with biobanking data, organizations must ensure justified data access for the right person. In this work, I illustrate the concept using a graph-based representation of contextual data with relational links, introducing four classes (“data owner,” “guest,” “authorized user,” and “authorized user for approved study”) to demonstrate how semantic relations can manage access protocols via the “accessed by” object property. This example fulfills the CAP requirements for traceability. However, it is intended as a demonstration. In a production-level security system, access control would typically involve group-based access, project-level permissions, and control over all content within specific projects. Figure 3.6 shows an example of a SPARQL query used to find all users accessing exome data files." 96 Figure 3.6 Using NGBO for data privacy and user access controls (A) A SPARQL query to find who can access a set of exome files. (B) An example of relations between individuals. (C) SPARQL query result, all individuals were linked to the set of exam data files through the relation “access by” selected in the variable?User. Here, “authorized user 002” has access to the approved data sets through “access,” while “Authorized user 001” cannot access the same dataset as there is no relationship established between the user and the dataset. This example is for demonstration purposes; in a production-level security system, access control would typically involve group-based access, project-level permissions, and control over all content within specific projects. 97 3.6 Discussion The findings from the SHGP and R2 Health Cluster use cases demonstrate NGBO’s practical value in enhancing data integration, discoverability, and metadata consistency. In SHGP, NGBO replaced manual data searches with ontology-driven querying, resolving inconsistencies in specimen metadata and enabling structured retrieval of sequencing data. In the R2 Health Cluster, NGBO standardized metadata across multiple grant-funded projects, reducing inconsistencies in key fields such as gene symbols and variant formats. Feedback from the data governance team noted a reduction in manual curation time and improved readiness for cross-study comparisons, affirming NGBO’s role in advancing semantic interoperability. Beyond technical and privacy aspects, ethical compliance remains essential when human-derived specimens are involved. Biobanks must ensure that specimen collection, storage, and reuse comply with informed consent provided by participants. Each specimen should be traceable to a consent agreement that defines its permitted use, duration, and data-sharing restrictions. To strengthen NGBO’s capacity for representing these ethical dimensions, future extensions could incorporate terms and relationships from the Informed Consent Ontology (ICO)—an OBO Foundry ontology designed to model consent processes, consent forms, and their relationships to research activities and data use. Integrating ICO concepts within NGBO would enable explicit linkage between consent metadata, specimen records, and institutional review board (IRB) protocols. This would enhance ethically compliant data integration and ensure that federated queries respect participant permissions and data-use limitations. 98 NGBO makes a distinct contribution to the biobanking domain by modeling omics contextual data alongside specimen and process metadata—something not fully addressed by existing ontologies. It unifies wet bench, sequencing, and analysis processes using standardized, interoperable terms, while supporting traceability and CAP-compliant documentation. This allows NGBO to bridge the gap between traditional biobanking systems and the demands of modern, data-intensive omics research. Aligned with FAIR principles, NGBO improves data findability through standardized terms registered in the Ontology Lookup Service (OLS), accessibility via consistent identifiers, and interoperability by linking with ontologies such as OBIB and OBI. Its use of logical constraints and structured annotations enhances reusability by reducing ambiguity and ensuring quality. NGBO also supports long-term archiving and structured retrieval of data and contextual metadata. Using NGBO identifiers, data files and associated information can be accessed via standardized query protocols, enabling federated queries across biobank systems. While the current version demonstrates successful integration and querying in SHGP and R2, broader deployment is needed to validate its performance in diverse biobank environments. NGBO does not yet include automated integration with EHR systems or dynamic privacy layers. Future development will explore adaptive access control mechanisms that apply sensitivity-based privacy settings across datasets, fulfilling real-world data governance needs. Finally, NGBO lays the groundwork for future applications in clinical research. By enabling integration with external resources such as ClinVar, it can support genomic annotation within 99 clinical workflows. This semantic linkage between biobank and clinical datasets has the potential to significantly enhance precision medicine and bridge the gap between research and clinical decision-making. Future work may explore automating data annotation using large language models and retrieval-augmented generation (RAG) tools. For example, RefAI (133), a GPT-powered system for biomedical literature retrieval and summarization, demonstrates how RAG methods can improve the precision and scalability of information retrieval. Adapting similar approaches to semantic data mapping in biobanking contexts could further extend NGBO’s utility by enabling intelligent, automated ontology-based annotation and querying at scale. 100 Chapter 4: Semantic web application for organizing and federated querying of biobank data 4.1 Author summary The exponential growth of genomic and biobanking data has significantly advanced biomedical research but also introduced new challenges for key stakeholders. Biobanking personnel face increasing demands related to data management, IT infrastructure, and resource constraints, while researchers encounter difficulties in discovering, accessing, and integrating data across fragmented systems. Traditional data management approaches, such as centralized data warehouses, are often inefficient, costly, and unable to handle the dynamic, distributed nature of biobank data. To address these challenges, this chapter introduces a semantic web-based data federation approach designed to streamline data discovery, integration, and access across multiple, geographically distributed biobank repositories. Unlike traditional systems, data federation enables real-time querying without the need for data centralization, reducing redundancy and ensuring data freshness. By leveraging semantic web technologies—specifically the NGBO—this approach facilitates interoperability, enhances data findability, and supports the FAIR data principles. Code and Data Availability The proposed web application—licensed under the Apache 2.0 open-source license and publicly available on GitHub at https://github.com/Dalalghamdi/NGBOSemanticWebApplication, 101 integrates diverse biobank datasets through a unified, ontology-driven interface. It provides federated querying capabilities powered by SPARQL, a user-friendly front-end designed for non-technical users, and robust access-control mechanisms to ensure data security and compliance with privacy regulations. By demonstrating how semantic web technologies can address the limitations of traditional biobanking data management, this application promotes scalable, interoperable, and ethically compliant data sharing and collaboration within the biomedical research community. 102 4.2 Introduction The purpose of this chapter is to present a federated querying tool developed as a proof of concept to address the challenge of fragmented biobank data sources. As explained in chapter 1 and 2, biobanks manage their data independently, using different formats, contextual data structures, and access mechanisms. This fragmentation limits researchers' ability to discover and retrieve data across institutions. To address this, I developed a semantic web application that supports querying across multiple distributed biobank endpoints without requiring centralized data storage. Instead of enforcing direct ontology-based integration, I have created a mapping layer using a standardized Excel template and a JSON script that allow data contributors to annotate and structure their data in accordance with the NGBO. This chapter describes the system architecture, implementation, and evaluation within the context of the SHGP, showing how the tool improves cross-biobank data discovery while respecting local data governance. Biobanks today receive data from a wide range of sources, each governed by different data sharing policies and technical standards. First, some data are generated directly by the biobank, primarily concerning the handling and storage of biological materials, as well as internal standard operating procedures. Second, data can be contributed by large cohort studies and submitted to population-based biobanks. Third, scientific studies may generate data using biobank materials and later return those results to the biobank. Fourth, routine healthcare systems—such as hospital information systems or disease registries (e.g., cancer registries) can be a source of clinical data linked to specimens. However, clinical data—especially when derived from EHRs—pose substantial privacy challenges, as they often include personally identifiable information (PII) and sensitive health records. This necessitates strict governance 103 and technical safeguards to ensure data de-identification, secure linkage with specimens, and compliance with local and international data protection regulations. Newly established forms of biobanks are now embracing specimen-derived data created by cutting-edge technologies, such as high-throughput technologies (34). As a result, the amount and complexity of stored data have significantly increased (40). Additionally, the six Vs of big data—value, volume, velocity, variety, veracity, and variability—are all present in biobanking datasets (134), contributing to the challenges faced by traditional data management approaches, such as conventional data warehousing (44). Centralized data warehouses, while historically common, pose significant limitations: they are time- and resource-intensive, often fail to reflect real-time updates (135), and require constant ETL (Extract, Transform, Load) processes to remain current (136). To address these limitations, more agile solutions are needed—meaning systems that are flexible, scalable, and capable of rapidly integrating and adapting to heterogeneous, distributed datasets (137). Decentralized and federated architectures that allow data to remain at its source—while still supporting harmonized querying and access—offer a more scalable and trustworthy approach to biobank data management. By doing so, organizations can enhance data accessibility, maintain local control over sensitive information, and ensure the timely availability of accurate and relevant datasets in the evolving landscape of biomedical research. Accordingly, the methods of biobanking data management have radically changed in recent years (138). There are already numerous biobanking data management tools, and new ones are 104 constantly being created. While these tools serve different institutional needs, their variety leads to increased fragmentation, as biobank data can differ in structure, semantics, access protocols, and storage locations. This heterogeneity poses challenges for both biobank personnel and users—especially when data are geographically distributed and maintained under different governance models. To address this, I propose adopting a federated querying approach that connects these disparate data sources without requiring centralization, offering researchers a unified access layer that respects local autonomy. In its most basic form, data federation systems (also known as federated database systems) aim to transparently map multiple databases into a single query interface (139). Such an interface uses query languages (e.g., SPARQL) to manipulate data over a unified global schema (e.g., ontologies), and query answering is achieved by data virtualization. This implies that all data are accessed through APIs and a global schema. The specific technological schema that facilitates federation can vary based on the technological and architectural choices made in the implementation process (140). Within a federated architecture, data remains within its appropriate jurisdictional boundaries, while the format of contextual data is centralized and searchable. The value of this approach lies in its ability to address both technical and ethical integration challenges. Technically, federation avoids the complexity of physically merging datasets by allowing data to stay at their original source. Ethically, it enables institutions to enforce local data governance policies—such as access restrictions and privacy rules—while still participating in broader data discovery efforts. Within a federated architecture, data remains within its appropriate jurisdictional boundaries, while the format of contextual data is centralized 105 and searchable. This balance of local control and centralized searchability supports secure, interoperable, and scalable biobank data integration across institutions. Data integration is a multiple-step process of finding similar entities and combining data from different sources into a non-redundant and unified view (141). In advance systems, data integration may encompass the use of advanced technologies such as data virtualization and semantic mapping to enhance interoperability and enable real-time access to integrated datasets. Combining the two concepts of data integration and federation resolves the difficulty of the physical distribution of the data and establishes a robust foundation for enhanced collaboration, efficiency, and the discovery of bio-specimen and bio-specimen-derived data. This chapter presents a proof of concept of how semantic web application for organizing and for the federated querying of omics-derived data stored in a biobank. The semantic web focuses on sharing, integrating, and reusing data through ontologies, linked open data, and knowledge graphs to ensure the correct meaning of shared data. The web application infrastructure comprises four main components: (I) SPARQL endpoints of data repositories registered in the data federation, (II) a friendly front-end interface where users can select from previously defined search values, (III) a back-end interface where the queries are generated automatically, and (IV) API coordinates between the graphical interface, the data repository endpoints, and the back-end. 4.3 Related work Over the last decade, several surveys have discussed the need for data integration approaches, paying particular attention to omics (142,143). More recently, this field has become an active 106 area for researchers due to increased information resources, resulting in critical achievements for community-driven integrated systems that automatically access data from multiple endpoints. Examples are BioMart (144), Bioschemas (145), and MOD-CO (146). Among these, federated query applications have emerged as a powerful approach for enabling real-time access to distributed biomedical data without centralized storage. Notable examples include BioFed (147) and the beacon project (116), each representing different implementation strategies for federated access. BioFed is a federated query processor designed for life science data, built on the ARDI (Autonomous Resource Discovery and Indexing) catalog. It dynamically indexes publicly available SPARQL endpoints to support life science queries. BioFed’s interface facilitates semantic querying but is limited to predefined query patterns, which users must construct using query elements provided by the indexed SPARQL endpoints. In contrast, the Beacon Project, developed by the Global Alliance for Genomics and Health (GA4GH) (148), offers a streamlined, privacy-aware federated protocol for genomic data discovery. A Beacon allows users to ask simple binary questions—such as "Have you observed this nucleotide at this genomic location?"—across a distributed network of genomic datasets. Responses are either “yes” or “no,” minimizing privacy risks while enabling large-scale federated searches without centralized data aggregation. Beacon also supports metadata disclosure (e.g., allele frequency or pathogenicity) under tiered access controls. Organizations can deploy Beacons individually or aggregate them into networks, such as the Beacon Network, enabling broad discoverability of genomic information while maintaining data sovereignty. 107 Both tools illustrate complementary approaches to federation: BioFed focuses on semantic integration of SPARQL endpoints with rich query customization, while Beacon emphasizes streamlined, privacy-conscious queries suitable for clinical and sensitive genetic datasets. Together, these examples highlight the value and versatility of federated architectures for biomedical research and offer guiding principles for the federated querying tool presented in this chapter. 4.4 Methods 4.4.1 Data structure, data source description, and query language Because the Semantic Web relies on open and interoperable standards, the core data in this system is stored as ontology-driven RDF statements, forming a graph-based model where nodes represent entities (e.g., information on a detected mutation), attributes correspond to data elements (e.g., gene name, location, amino acid change), and edges indicate relationships between entities. This RDF structure enables flexible, semantic representation of omics-derived biobank data. To ensure selective access and data privacy, access control policies are also expressed using RDF statements. These policies define which entities may be accessed and by whom, using the relation “accessed by” to indicate permissions. The access model follows the same methodology presented in Section 3.5.2 and illustrated in Figure 3.6, which outlines how entity-level access control can be modeled using RDF triples. This approach also supports the definition of extended access roles beyond the default configuration. Authenticated users with proper credentials may 108 be granted permission to view one or more specific entities, thereby preserving data governance at the source level. To facilitate the onboarding of new data sources, I developed a standardized Excel sheet and an accompanying JSON template that define the structural mapping rules based on the latest NGBO specifications. Contributors can populate the Excel file with their local data, which can then be automatically converted into RDF triples. The JSON template serves as a machine-readable structural descriptor, specifying the relationships among entities and guiding the RDF conversion process. These structural descriptions define how entities are semantically connected, ensuring consistent and interoperable integration into the federated system. Datasets: Setting: Each dataset used in this project was hosted in a separate GraphDB https://graphdb.ontotext.com/) endpoint, which served as the core infrastructure for managing RDF-based biobank data. GraphDB supports semantic linking, indexing, and enrichment of diverse datasets and is enterprise-ready with full compliance to W3C standards. These datasets contain structured instances of omics-derived contextual data and were the same datasets used during the NGBO evaluation by competency questions and reasoners described in Section 3.4.4.2. The data were imported into the NGBO OWL ontology using the Celfie plugin, and false-positive and false-negative test instances were also included to assess reasoning behavior. Each dataset was populated with real and simulated biobank entries to test clarity and consistency principles using HermiT and Pellet reasoners. This ensures that the federated query tool built in this chapter operates on datasets that have been pre-validated for semantic coherence 109 and logical consistency. The datasets are accessible programmatically through APIs, allowing them to be queried dynamically within the federated architecture described in this chapter. Queries and evaluation: To evaluate the effectiveness of the federated search tool, I developed a structured interview protocol consisting of two sets of questions: The first set was derived from the competency questions (CQs) introduced in Section 3.2 and detailed in Supplementary Method B.1. These questions were used to assess whether the system could successfully retrieve relevant data across federated endpoints, such as identifying specific variants, filtering results by platform, or tracing sample provenance. The second set focused on user experience (UX) evaluation, assessing the ease of use, clarity of the interface, perceived utility, and the practicality of the federated approach for biobank users. The interviews were conducted with seven professionals affiliated with King Fahad Medical City (KFMC), the Saudi Human Genome Project (SHGP), and SHGP partner institutions:  A scientist from SHGP  The IT manager at the Saudi Diagnostic Lab (SHGP partner)  An automation specialist from KFMC  A biobanking technologist from KFMC  A bioinformatics consultant from KFMC  Two research specialists from KFMC 110 This evaluation setup allowed for both validation of the tool’s querying capabilities and qualitative insights into its usability and potential for integration into real-world biobank workflows. 4.4.2 Access restriction aspects of data federation using semantic web tools. Data security is a paramount concern in modern biobanking, encompassing protection against breaches of confidentiality and availability (150). Data confidentiality refers to safeguarding information from unintentional, unlawful, or unauthorized access (151). In contrast, data availability breaches relate to incidents that result in the loss, destruction, or inaccessibility of PIIs. While various security challenges exist in open biobanking environments, the most pressing concern is ensuring the protection of participant information from such confidentiality and availability threats. Within the context of biobanking, IT security plays a crucial role in protecting participants, with access restriction being a key countermeasure (152) (153). It is mandatory for data custodians to control access to specific local source variables—particularly those containing sensitive or identifiable information. In general, access control refers to a framework that defines how data access permissions are structured and enforced (154). Semantic web technologies have been increasingly adopted in designing such frameworks to assist data producers in protecting their resources from unauthorized use (155,156). These methods leverage RDF and SPARQL to formally represent access control policies and support enforcement mechanisms. 111 Research on RDF data access control can be categorized into several approaches, including:  Named graphs (155,158,159),  Rule-based systems (160), and  Ontology-based access control (161). Each approach enables different levels of security granularity. While most systems simply grant or deny access, recent studies have explored more nuanced mechanisms such as incomplete or granulated result sets for users with limited access privileges (162–165). In this work, I employed an ontology-based access control model tailored for decentralized systems. In this model, access is granted based on defined relationships among users, data entities, and access roles—all identified by HTTP URIs. This fine-grained approach allows for flexible and precise control over who can access which parts of the dataset, aligning with the federated and privacy-preserving nature of the system. 4.4.3 Back-end development The back end of the web application is designed to support the full end-to-end functionality required for federated querying and semantic integration across distributed biobank data sources. It adheres to the end-to-end design principle (149), ensuring that core logic and processing are implemented as close to the data source as possible, thereby improving system efficiency, scalability, and security. The back-end architecture is built using Python, which orchestrates the various components involved in query formulation, dispatch, and results processing. A core component of this layer 112 is the SPARQLWrapper library—a Python wrapper for a remote SPARQL endpoint. SPARQLWrapper enables the application to dynamically construct and send SPARQL queries to one or more endpoints and parse the resulting responses in various machine-readable formats such as JSON or XML. The language of federation is SPARQL, which allows users to query heterogeneous RDF datasets using a unified query syntax. All queries are routed through HTTP-based SPARQL endpoints, each corresponding to a distinct registered data source within the federation. The system architecture supports two modes of querying: (1) targeted querying, where the user selects specific endpoints based on metadata criteria, and (2) broadcast querying, where the same query is simultaneously dispatched to all available endpoints. This flexibility is critical for balancing specificity and completeness depending on the research scenario. To ensure that each endpoint can interpret queries consistently, all queries are dynamically assembled using NGBO-aligned terms and relations, guaranteeing semantic interoperability across the federation. The JSON-based mapping templates (introduced in Section 4.4.1) are used to construct the appropriate RDF triple patterns for each query. Once executed, the results from different endpoints are aggregated, normalized, and presented to the user through the front-end interface. 4.4.4 Graphical user interface (frontend development) The primary objective of the web application's graphical user interface (GUI) is to provide biobank users—particularly those without technical expertise—with a clear and easy-to-use 113 platform for discovering and querying integrated omics-derived datasets. The interface was designed to support a wide range of user needs, from researchers looking for specific sequencing data to data managers verifying sample availability. It was implemented using HTML, Bootstrap, and JavaScript, ensuring compatibility across standard web browsers without requiring additional technical setup. The front end was implemented using HTML, Bootstrap, and JavaScript, offering a lightweight, browser-compatible experience. The GUI comprises five main sections, each tailored to a specific function within the federated querying workflow:  Biobank Data Registry Navigation This section acts as an exploratory dashboard where users can browse available biobank data sources. Each registry provides metadata about the scope, data types, and sample coverage of the respective biobank. This enables users to answer questions such as: “What types of omics data does this biobank store?” or “Are sequencing assay results available for lung cancer patients?”  Template-Based Query Filters (Ontology-Driven) This section allows users to construct queries by selecting from predefined, ontologized dropdown menus that correspond to NGBO-aligned data fields (e.g., variant type, sequencing platform, specimen type). By abstracting SPARQL syntax into human-readable templates, users can formulate powerful semantic queries without needing to write code.  Query Results Visualization Panel Once a query is submitted, this section presents the results in an interactive table or chart format. It supports highlighting, sorting, and filtering and allows users to explore specific attributes (e.g., 114 mutations, allele frequency, sample ID) returned from federated endpoints. This visual feedback is crucial for interpreting search outcomes quickly.  Specimen Request Interface This section allows users to submit formal requests to data owners for access to specific specimens or datasets identified through the query interface. It includes request justification, usage terms, and optional attachments. Requests are routed through a secure backend channel to the relevant data custodian for review and response.  User Registration and Authentication System A dedicated module handles user registration, login, and role-based access control. Depending on their role (e.g., researcher, data owner, reviewer), users are shown different interface elements and given varying degrees of data access. This system integrates with the RDF-based access policies described in Section 4.4.2 to enforce fine-grained control. 4.5 Results 4.5.1 Excel sheet template for standardizing biobanking data attributes As part of the data harmonization framework, a structured Excel template was successfully developed and implemented to standardize the collection of contextual metadata across biobank data sources. The template, shown in Figure 4.1, was used by partner institutions to input key attributes such as specimen labels, biobank names, sequencing protocols, and assay types. Each column in the sheet corresponds to an NGBO-aligned field, and data entry was guided by dropdown menus and cell-level descriptions to ensure consistent use of controlled terms. 115 Once completed, the filled templates were converted into RDF triples using the Celfie plugin and subsequently ingested into GraphDB endpoints. These records became part of the federated querying infrastructure described in earlier sections and were successfully queried across multiple endpoints. The data standardized through this template demonstrated high alignment with the FAIR Guiding Principles, as evaluated using the FAIR Implementation Profile (FIP) framework:  Findability was achieved through the use of persistent, ontology-based identifiers in each metadata field, enhancing machine-readable indexing and discovery.  Accessibility was supported through API-exposed GraphDB endpoints and RDF outputs queryable by standard protocols.  Interoperability was ensured by restricting values to terms from NGBO and related OBO Foundry ontologies (e.g., OBI, IAO), enabling semantic consistency across sources.  Reusability was improved by embedding clear field-level definitions and standardized descriptors that reflect domain best practices. 116 Figure 4.1 Excel sheet template Structured Excel sheet used for collecting standardized contextual metadata from biobank contributors. Columns reflect ontology-aligned fields such as specimen source, biobank ID, laboratory protocol, and sequencing assay type. Each field is linked to a defined NGBO class or property, enabling automatic RDF conversion. Built-in tooltips and examples reduce annotation ambiguity. The template operationalizes FAIR principles by embedding persistent identifiers, clear semantics, and structured relationships, thereby enabling automated discovery and integration across federated SPARQL endpoints. 4.5.2 Backend and front-end (graphical user interface) development Figure 4.2 illustrates the architecture of the federated query engine implemented in this project. When a user selects keywords from the search interface—mapped to predefined ontology terms—these selections are passed to the query engine, which handles query decomposition and source selection. This component constructs SPARQL queries based on available endpoint metadata and forwards them to the appropriate biobank data sources. Although the interface currently supports fixed query templates aligned with the NGBO ontology, advanced users can manually customize SPARQL queries within the open-source codebase. Predefined transformation rules, append or update the ontology individuals, data properties and object properties. Drop down menu of ontologiesd selections Definitions and instructions for users to enter data 117 The returned results undergo result reconciliation, where responses from multiple SPARQL endpoints are aggregated and formatted for display in the front-end interface. Each step—from query dispatch to data display—is mediated by the SPARQLWrapper API, which handles formatting, endpoint access, and error management. Data access is controlled by both user roles and variable-specific permissions: User-based access control: Access is assigned according to user type. Guest users are limited to non-sensitive, summary-level queries (e.g., ASK or COUNT queries), while registered users with approved access may view detailed contextual metadata. As shown in Figure 4.3, the system enforces login credentials. Once logged in, users are directed to the query interface as shown in Figure 4.4 where they interact with template filters. Results returned to users with higher permissions include specimen metadata and linked contextual data as shown in Figure 4.5. Variable-level access control and approval workflow: Approval decisions are handled manually at this stage; the system records the request, notifies the custodian, and—if approved—updates the user’s access status for the requested resource. Role updates are then propagated to the access control layer of the system, allowing seamless re-querying with newly granted permissions. It is important to emphasize that data federation occurs across autonomously governed sources. Each data contributor maintains full control over data availability, sharing policies, and approval workflows. The system is designed to support these local governance boundaries while enabling semantic querying across all registered nodes. 118 Figure 4.2 Architecture of the Federated Query Engine System architecture of the federated query engine supporting biobank data discovery. The workflow begins with user interaction through the web application (top), where queries are constructed using template-based ontological filters. These queries are processed by a backend engine that performs decomposition, endpoint selection, and SPARQL dispatch. Results are returned, reconciled, and visualized. Additional layers handle request routing for access approvals and endpoint registration. Example interface screenshots show: (top-right) the front-end filter dashboard; (middle) backend query logic using Flask and SPARQLWrapper; (bottom-right) example endpoint metadata including source description and SPARQL access URL from the Saudi Human Genome Project (SHGP). 119 Figure 4.3 Illustration of the initial login page The interface supports two modes of access: registered user login (via email and password) and guest access. Authenticated users gain access to protected metadata fields based on their assigned roles (e.g., researcher, data owner), while guests are limited to aggregate-level querying functions (e.g., YES/NO or COUNT results). This access control mechanism aligns with the ontology-based permissions model described in Section 4.4.2, enabling role-sensitive discovery while preserving local data governance policies. Figure 4.4 Illustration of the filter selection Users are presented with ontology-driven dropdown menus populated with controlled vocabulary terms derived from the Next Generation Biobanking Ontology (NGBO). In this example, the user selects “cerebrospinal fluid specimen” from the “specimen type” filter. These filters correspond to NGBO-aligned data fields and are dynamically mapped to SPARQL query parameters. This interface allows non-technical users to construct semantically rich queries across distributed biobank data sources without writing code, supporting the FAIR principles of machine-actionable, interoperable metadata. 120 Figure 4.5 Visualization of the query results The top section displays the output returned to a guest user—restricted to aggregate-level results such as a count of matching records (e.g., number of available sequencing entries). In contrast, the bottom section shows the detailed query results accessible to a registered user with approved permissions, including specimen labels, biobank identifiers, sequencing protocols, and data file formats. These fields are annotated using NGBO-aligned ontology terms. This differential access reflects the ontology-driven access control model described in Section 4.4.2, supporting secure and role-sensitive data discovery across biobank endpoints. 121 4.5.3 Evaluation Currently, the federated registry contains more than 130 MoC records describing resources from two organizations for the testing phase: King Fahad Medical City (KFMC) and the Saudi Human Genome Project (SHGP). These records included structured contextual metadata covering specimen processing workflows, bioinformatics analyses, and associated software, all annotated using the NGBO ontology. To assess the effectiveness of the web application, structured interviews were conducted with seven professionals from KFMC and SHGP institutions. Each participant was presented with a series of questions derived from the competency questions (CQs) introduced in Chapter 3 and detailed in Supplementary Method B.1. The goal was to determine whether the federated system could accurately return relevant results based on specific biomedical search tasks. All seven participants were able to answer five of the six questions correctly. The only question that was not consistently answered was: 6. Search for reference genome information This question received the lowest correct response rate, as reference genome information was not included in the current version of the filter set available in the user interface. This limitation will be addressed in future updates of the filter options to ensure full coverage of NGBO-based query fields. In addition to technical evaluation, participants were asked about their experience with the interface, perceived usefulness, and potential improvements. Five out of seven participants expressed strong satisfaction with the tool and highly recommended its adoption, particularly 122 citing its ability to unify access to distributed biobank data sources. Three participants suggested expanding the system by registering more data providers. One participant—the IT manager at the Saudi Diagnostic Lab (SHGP partner)—expressed some discomfort with transitioning from their existing data management system. Their primary concern was cybersecurity, particularly regarding exposure of internal systems to federated querying protocols and API-level access. This feedback reflects a broader concern among institutional IT departments regarding cyber threat prevention and assurance in federated, API-exposed infrastructures. These concerns directly inform future development plans described in Chapter 6, specifically Section 6.1, where I propose the implementation of fine-grained healthcare data access control and cyber threat prevention using graph databases. This future work—backed by an approved grant—aims to enhance system resilience by integrating policy-driven access enforcement, attack surface minimization, and automated auditing within the federated architecture. Such advancements are critical to building institutional trust and supporting secure adoption of federated querying tools in sensitive biomedical environments. 4.6 Discussion Centralized biobanking systems are siloed, separating records from key stakeholders and limiting their utility. As technologies, protocols, and metadata standards evolve, centralized repositories frequently fall out of sync with current practices, leading to fragmented records. In this chapter, I demonstrated how data federation using semantic web technologies offers a sustainable 123 alternative—one that enables cross-institutional data discovery without requiring centralized data storage or loss of local governance. The federated tool I developed as a proof of concepts provides a scalable framework that integrates semantically enriched biobank metadata from distributed endpoints. It allows biobanks to maintain control over their data while still enabling query execution across institutional boundaries. This model is particularly important in the context of sensitive biomedical data, where data localization policies, privacy regulations, and institutional autonomy are critical factors. Looking ahead, I plan to extend this federation initiative by involving more institutional partners and enhancing the technical capabilities of the platform. These efforts will focus on the following areas:  Support for additional metadata formats (e.g., JSON): While RDF and OWL formats are ideal for semantic web applications, many institutions store data in lightweight formats like JSON. By supporting these, the system will become more interoperable and accessible for data contributors with limited semantic web infrastructure. This aligns with FAIR principles, particularly Interoperability and Reusability.  Automated mapping from SQL databases to OWL ontologies: Many biobank data systems are relational (SQL-based). Automating the transformation from SQL to OWL would remove a major technical barrier and enable legacy systems to participate in the federation. 124  Performance optimization: As the number of endpoints and queries grows, system responsiveness and stability become critical. Performance improvements will include query caching, smarter source selection, and asynchronous API handling to ensure that the platform remains usable at scale, especially in research environments with high query volume. Additionally, based on stakeholder feedback—particularly concerns raised by institutional IT leads (see Section 4.5.3)—the next phase of development will place a strong emphasis on security, trust, and compliance. This will be pursued through the grant-funded initiative presented in Chapter 6, which focuses on fine-grained access control and cyber threat prevention using graph databases. These enhancements aim to address real-world constraints around cybersecurity, which are essential for fostering institutional trust and long-term participation in federated networks. In summary, the federated approach not only overcomes the limitations of traditional centralized systems but also offers a practical, flexible, and secure model for biobank data integration. With ongoing refinement and broader engagement, it has the potential to become a national infrastructure supporting FAIR, ethical, and scalable reuse of biomedical data in Saudi Arabia and beyond. 125 Chapter 5: Leveraging ontologies for biomedical text mining 5.1 Author summary A significant portion of biomedical information is stored as text. More than 3000 new articles, which contain valuable textual information on recent insights and discoveries, are published daily in peer-reviewed journals. EHRs are an additional vital source of clinical data containing important information buried in unstructured text. To address the challenges posed by unstructured text, text mining aims to extract information and knowledge and present it more explicitly. This process is facilitated by utilizing computational technologies such as ML, natural language processing, statistics, computational linguistics, and semantic web technologies (e.g., ontologies), thereby enhancing the understanding and mining of textual data. Ontologies, which provide a vocabulary for terms within a specific domain, play a crucial role in applications relying on domain-specific terminologies. Text mining is a notable example of such an application, and it has rapidly evolved in recent years. Text-mining systems can reliably tag entities and relations in unstructured text and map them to standardized labels in ontologies. Successful examples of ontology-based text mining applications include identifying candidate genes for disease, identifying drug targets and indications, and identifying the presence of disease models based on phenotypes. This review provides an overview of the ontologies employed for biomedical text mining by focusing on tasks and methods developed over the past years and describes methods that can be adapted to match unstructured biobank text to ontologies to improve and automate data harmonization. Furthermore, the review identifies the key limitations and barriers to using domain ontologies in text-mining tasks. 126 5.2 Introduction While the preceding chapters focus on the development and application of ontologies to enhance the discovery and integration of biobank data, ontology-driven methods have also demonstrated substantial value in other domains of biomedical informatics. One such area is biomedical text mining, where ontologies are increasingly used to improve information extraction, semantic annotation, and knowledge discovery from unstructured textual data. To illustrate the broader impact and versatility of ontological approaches in supporting data-driven biomedical research, this chapter provides a structured review of how ontologies have been applied in key biomedical text mining tasks. By examining the methodological parallels and complementary goals of these applications, this chapter situates ontology-enabled text mining as an integral part of the broader ecosystem of semantic technologies explored throughout this thesis. The new digital era has brought an exponential increase in the amount of information stored as text in digital repositories. Among the fields that have experienced a surge in information growth is the biomedical field (150). A substantial portion of this growth stems from scientific research outputs—such as journal articles, conference proceedings, and grant proposals—which disseminate new discoveries and theoretical advancements among researchers (151). In parallel, healthcare systems generate vast amounts of clinical text, including physician notes, discharge summaries, and EHR capturing patient-specific data and clinical decision-making processes. Both scientific and clinical texts offer valuable insights for advancing biomedical research and improving healthcare delivery. In biobanking as well, large portions of specimen metadata and contextual information are often stored as free text, creating similar challenges for integration and reuse. However, the nature and sheer volume of this textual data have created significant barriers 127 to efficient knowledge retrieval and utilization (152), including large volumes of text, inconsistent terminology, semantic ambiguity, and the challenge of integrating information across diverse biomedical sources. Text mining aims to extract information and knowledge from unstructured or semi-structured text and present it in an explicit, structured form (153). Unstructured text refers to human language data that lacks a predefined model or consistent format (151), such as free-text narratives, clinical notes, or scientific articles. Semi-structured text contains partial organization, such as metadata templates, facilitating easier parsing. Text mining has become an essential tool in biomedical research, enabling the information retrieval and the discovery of novel associations, hidden patterns, and testable hypotheses from unstructured data (154). Despite these advancements, biomedical text mining continues to face persistent challenges, especially in dealing with ambiguous and variable language used across sources. Biomedical texts often contain abbreviations, synonyms, and spelling variations, and many terms carry different meanings depending on the context in which they appear. These characteristics make it difficult for automated systems to accurately identify, normalize, and relate key entities within and across datasets. Recently, a growing number of studies have begun incorporating ontologies to enhance semantic understanding in biomedical text mining applications (155–159). These ontology-based approaches have proven effective in diverse tasks such as drug-drug interaction extraction using the Chemical Entities of Biological Interest (ChEBI) ontology (6), automated annotation of gene 128 products with Gene Ontology (GO) terms (7), information extraction (8), and the prediction of human phenotype-protein associations (9). Ontologies have also improved core Natural Language Processing (NLP) tasks like text classification (16,17), information extraction (18), and named entity linking (19), where domain-specific semantics and disambiguation are critical. Ontologies are formal representations of domain knowledge, consisting of standardized concepts and their relationships, designed to support consistent data annotation, integration, and automated reasoning (15). They represent information in a structured way that follows clear rules and definitions, using standardized vocabularies and frameworks such as the Web Ontology Language (OWL) (89)—a semantic web standard for modeling complex knowledge and relationships between concepts. This formal structure enables machine-readable semantics by interlinking concepts through well-defined relationships—for example, specifying that 'amoxicillin' is_a 'antibiotic', or that it treats 'bacterial infection'. These relationships support key applications such as automated reasoning, semantic search, entity linking, and data integration, while also enabling systems to draw new conclusions and detect inconsistencies (160). Unlike simple terminologies or controlled vocabularies, ontologies allow logical inference. For instance, if an ontology specifies that 'amoxicillin' is a type of 'antibiotic', and a system extracts a prescription mentioning amoxicillin from a clinical note, it can automatically classify the medication under the broader category of antibiotics—even if the term 'antibiotic' is never explicitly stated. This capacity to reason over hierarchical relationships enables more comprehensive data interpretation and retrieval in biomedical applications. 129 Ontologies capture not only simple hierarchies but also a wide range of relationships between biomedical concepts, enabling richer semantic understanding. Foundational relationships—such as taxonomical (‘is_a’, or subclass of) and compositional (‘part_of’, or component of)—can be expressed through an ontology. More complex and domain-specific relationships, such as ‘clinically-associated-with’ or ‘causes’, can also be modeled to capture biomedical-specific semantics (161). By converting unstructured or semi-structured text into structured representations underpinned by ontology, these relationships enable automatic semantic interpretation, leading to more accurate data extraction and annotation. Building on these structural foundations, ontologies are increasingly being integrated into advanced text mining systems to support concept recognition, disambiguation, and semantic enrichment of biomedical documents. These systems combine machine learning techniques with ontology-driven reasoning to address persistent challenges in biomedical text mining, including synonym and abbreviation handling, ambiguity in entity recognition, lack of contextual semantic structure, and difficulties in integrating heterogeneous biomedical data sources (162). By grounding extracted information in a formal conceptual framework, ontologies support consistent knowledge representation and improve interoperability across clinical and research domains. In this chapter I explore how ontologies have been integrated into key biomedical text mining tasks, including natural language processing, text classification, information extraction, named entity linking, embedding-based modeling, and relation extraction. Our goal is to provide a structured overview of the evolving role of ontologies in biomedical text mining, highlight 130 common integration strategies, and identify open challenges and opportunities for future research. 5.2.1 Preliminaries: Key Biomedical Text Mining Tasks and the Role of Ontologies Biomedical text mining involves a collection of computational tasks designed to extract structured information from large volumes of unstructured or semi-structured clinical and scientific text (163). These tasks include NLP, text classification, information extraction, named entity linking, and relation extraction. While many of these tasks can be performed using traditional statistical or machine learning approaches, recent studies have demonstrated that integrating domain ontologies can enhance performance by introducing semantic consistency, domain constraints, and interpretable outputs. The following sections contrast conventional and ontology-enhanced approaches to each task, highlighting the conceptual advantages of ontology integration. 5.2.1.1 Natural language processing (NLP) Natural Language Processing (NLP) refers to computational methods that enable computers to read, understand, and interpret human language (164). In the biomedical domain, NLP is commonly used to process unstructured text content such as clinical notes, discharge summaries, and scientific publications (165). These texts contain rich clinical and scientific information but are challenging to analyze systematically because they do not follow a consistent structure. Core NLP tasks include identifying word boundaries (tokenization), assigning grammatical roles like noun or verb to each word (part-of-speech tagging), analyzing sentence structure (parsing), recognizing important entities such as diseases or drugs (named entity recognition), and interpreting meaning based on context (semantic interpretation). These functions support 131 practical applications such as clinical coding, automated documentation, literature summarization, and information retrieval (164). Traditional biomedical NLP systems often rely on either hand-crafted rules developed by experts or machine learning models trained on large, annotated datasets (166). While these approaches have led to many successful applications, they also present limitations. Rule-based methods require intensive manual effort and may not generalize well, while machine learning models depend heavily on high-quality training data and often struggle to capture the deeper semantic meaning of biomedical language. To address the limitations of traditional NLP approaches—such as reliance on manually rule-based techniques, dependence on annotated data, and limited semantic understanding— modern systems increasingly incorporate ontologies. These ontologies encode biomedical concepts and their relationships in a structured, machine-readable format, forming the foundation for more accurate, consistent, and semantically rich interpretation of text (167). They help overcome the challenge of limited semantic understanding by making the meaning of biomedical terms and their relationships explicit and computable. Thus, ontology-based NLP adds a semantic layer to text analysis—that is, a structured understanding of meaning—by linking words and phrases to formally defined biomedical concepts and the relationships between them (168). This enables systems to move beyond keyword matching and surface-level interpretation toward deeper conceptual understanding of biomedical language. Ontologies also help define the structure of information extracted from text by providing consistent labels for biomedical concepts—such as types of entities (e.g., diseases, drugs), relationships (e.g., causes, associated_with), and document sections (e.g., diagnosis, treatment) 132 (169). This consistency allows systems to identify that terms like “aspirin” and “hypertension” refer to a drug and a disease, respectively, and that the phrase “aspirin treats hypertension” expresses a semantically valid relationship. Ontologies also enforce rules—known as domain and range constraints—which specify which types of concepts can be related and how. For instance, a “drug” can treat a “disease,” but not vice versa, helping systems avoid misclassification and improve interpretability (170). In this way, ontologies replace many manually crafted rule-based techniques by embedding structural and logical constraints directly into the system. By embedding expert knowledge directly into structured vocabularies and relationships, ontologies reduce the need for rule-based methods. They also provide semantic context that helps systems generalize even when annotated training data is limited and enable more accurate interpretation by grounding terms in well-defined biomedical meanings. In some cases, ontology instances serve as lexical resources for entity identification, while formal languages such as OWL and query languages like SPARQL Protocol and RDF Query Language (SPARQL) enable rule-based inference for relation extraction and tagging. As illustrated in Figure 5.1, ontology components—such as standardized vocabularies, formally defined relationships, domain and range constraints, and logic-based inference—enhance core NLP tasks like named entity recognition and semantic interpretation. These structured outputs support downstream applications such as clinical coding, summarization, information retrieval, and semantic search. 133 Figure 5.1 Integration of ontology, annotation tool, and NLP Ontology components—such as standardized vocabularies, formally defined relationships, domain and range constraints, and logical inference through OWL and SPARQL—are integrated into NLP pipelines to enhance tasks like annotation, named entity recognition, relation extraction, and semantic interpretation. These enriched outputs support downstream applications including clinical coding, summarization, information retrieval, semantic search, and question answering. 134 In conclusion, ontology-based NLP has demonstrated effectiveness in many different domains. In the financial sector, Altinok et al. developed an ontology-based dialogue system for banking and finance chatbots. The system used a domain ontology to drive dialogue management, resolve anaphora, and maintain conversation state by explicitly linking user utterances to ontology concepts. Natural language understanding components extracted intents, key phrases, and grammatical structures, which were semantically matched to ontology classes and instances—enabling interpretable, structured responses without reliance on large, annotated datasets (171). Similarly, in the legal field, Sleimi et al. developed a rule-based and machine learning–assisted framework for extracting semantic metadata from legal documents. Their system, which structured legal texts around standardized metadata types defined in an ontology, achieved high precision (97.2%) and recall (94.9%). These examples highlight the broader applicability and practical value of ontology-based NLP methods in highly technical and domain-specific contexts (172). 5.2.1.2 Text classification Text classification, also known as text tagging or text categorization, involves assigning pre-specified concepts to a text based on its content. In other words, it’s the process of deciding what a document is about. (173). For example, in the biomedical domain, this could mean classifying a clinical note by disease type, a research article by focus, or a report by its publication intent. Text classification is widely used in applications such as disease surveillance, information retrieval, and clinical decision support (170,174). Traditional classification methods use machine learning algorithms trained on labeled examples. These systems typically involve several steps, such as representing the content of documents in numerical form, selecting relevant features or transforming them, and creating vector-based representations before applying classification 135 models (175–177). While effective, such approaches require large annotated datasets and often struggle with challenges like synonymy (different words with the same meaning), polysemy (the same word having multiple meanings), and the disconnect between textual expressions terms and their underlying biomedical concept (178) Ontology-enhanced text classification improves these systems by incorporating domain knowledge encoded in structured vocabularies. Ontologies group related concepts together in a meaningful way, allowing classification models to recognize connections between terms that might not appear similar on the surface (179). Unlike traditional methods that depend heavily on large labeled datasets, ontology-based classification can perform well even with limited training data, as it uses built-in relationships within the ontology to estimate similarity and generalize concepts (180). Techniques such as semantic similarity scoring, term expansion (including broader or related concepts), and mapping phrases to conceptual neighborhoods help the system gain a deeper understanding of biomedical language—especially when dealing with complex phrases or abstract categories (174,179).This semantic awareness supports more accurate and interpretable classifications, even in the presence of ambiguous or varied language. For instance, in the domain of materials science, the MaterioMiner system uses the Materials Science Ontology to map diverse terminology—such as “annealing” and “quenching”—to the broader concept of “heat treatment.” By grouping related terms under consistent categories, the system improves the accuracy of classifying scientific literature, despite inconsistent or varied language across documents. This approach demonstrates how ontologies support generalization and concept-level normalization, enhancing classification performance in highly technical domains (181). 136 5.2.1.3 Information extraction Information extraction refers to the automated identification of specific types of concepts or facts within a given domain, while ignoring unrelated information. It focuses on recognizing and extracting relevant details from unstructured or semi-structured text—text that lacks consistent formatting but may contain partial organization, such as headers or templates (182). The goal of information extraction is to retrieve occurrences of particular entities, events, or relationships embedded within textual content (183). For example, in a clinical context, information extraction might target elements such as patient age, reason for referral, and diagnosis from physician notes. To perform this task, information extraction systems use various computational methods, including rule-based approaches (which rely on predefined patterns), pattern matching, and statistical models that learn from examples (184). Figure 5.2 shows how a free-text clinical narrative can be automatically transformed into structured data—extracting fields such as age, gender, reason for referral, and diagnosis—supporting downstream tasks like decision support and reporting. 137 Figure 5.2 Example of information extracted from physician note The unstructured clinical text is parsed into discrete, structured fields (e.g., age, gender, diagnosis), enabling downstream use for clinical decision support, reporting, or data integration. Ontologies are particularly useful in information extraction because they provide formal and explicit representations of concepts within a domain. This structured knowledge supports consistent identification and interpretation of relevant information in text (182). A specialized area known as Ontology-Based Information Extraction (OBIE) has emerged, integrating ontology-driven reasoning into the information extraction pipeline. Unstructured or semi-structured text—such as clinical notes or research abstracts—is processed using rules or machine The patient, a 45-year-old male, was referred to our clinic by Dr. Smith for persistent abdominal pain and digestive issues. He reported a six-month history of intermittent discomfort in the upper abdomen, often accompanied by bloating and changes in bowel habits. Clinical examination revealed tenderness in the epigastric region, prompting further diagnostic workup. Subsequent tests and upper gastrointestinal endoscopy led to a diagnosis of gastritis and mild esophagitis, with a treatment plan initiated for symptom management. (This text is generated by AI) Age: 45. Gender: Male. Reason for referral: abdominal pain, digestive issues. Diagnosis: gastritis and mild esophagitis 138 learning techniques that are guided by ontologies. These ontologies help the system recognize important terms (like diseases, symptoms, or treatments), understand how they relate to each other, and organize this information into a structured format that computers can interpret and use (182). Compared to traditional information extraction methods, OBIE systems produce outputs with richer semantic meaning (185). This makes the extracted information easier to understand and share between different systems, a feature known as interoperability enhancing interoperability and enabling integration with broader semantic web infrastructures (186). This is especially important in modern biomedical computing, where many applications rely on information that is consistently labeled and formatted—also known as semantic annotation—so that it can be automatically processed by computer programs or intelligent software agents (187–189). Ontologies contribute to information extraction by ensuring the consistent use of domain-specific terms and by defining the types of entities and relationships that can appear in a given context. They also specify rules—called domain and range constraints—that govern how entities can be linked. In some OBIE systems, these ontology structures are used to automatically generate rule sets using formal languages such as OWL and SPARQL. In others, ontologies support machine learning models by offering semantic context—for example, by organizing concepts into categories or highlighting relationships between terms. Additionally, OBIE systems can help improve the ontologies themselves by using newly extracted information to update or validate the content over time. A successful example of OBIE can be found in the manufacturing domain, where a production ontology was developed to extract structured process descriptions from semi-structured technical documents. This approach enabled the semantic annotation of manufacturing 139 operations—such as steps, materials, and tools—facilitating consistent tagging, easier querying, and improved interoperability across industrial systems. By embedding expert knowledge into formal structures, the system enhanced the retrievability, reuse, and reasoning capabilities over production data (190). 5.2.1.4 Named entity linking Named entity linking is a core component of biomedical text mining systems. A named entity refers to a real-world object (191), such as person, organization, specimen, and devices. Named entity linking, also referred to as named entity normalization, involves two main steps: first, recognizing entities mentioned in the text (often handled by named entity recognition); and second, linking each recognized mention to its corresponding concept in a structured knowledge base or ontology (192). Traditional named entity linking methods often rely on string matching, frequency-based heuristics, or statistical co-occurrence. While these approaches can work well in narrow domains, they often struggle with challenges common in biomedical texts, such as ambiguity, synonyms, abbreviations, and terms with multiple meanings. Ontology-enhanced named entity linking addresses these limitations by incorporating formal semantic structures. In this approach, each entity mentioned in the text is mapped to a corresponding concept in the ontology by considering both the surrounding context and the ontology's internal structure (193,194). Ontologies improve disambiguation by providing semantic neighborhoods (groups of related concepts), hierarchical relationships (e.g., subclass links), and property constraints that help distinguish between similar but distinct entities. 140 In addition, ontologies support richer handling of synonyms and variants, allowing the system to recognize that terms like “myocardial infarction” and “heart attack” refer to the same concept, or that “HER2+ breast cancer” is a subtype of “breast cancer.” This semantic richness makes ontology-based entity linking more robust and interpretable, especially in biomedical domains where terminology is complex and frequently evolving. 5.2.1.5 Embedding and pre-training Entity normalization in biomedical text mining is closely linked to the principle of semantic relatedness, which refers to the idea that different terms may refer to the same or similar concepts. Semantic similarity, however, is not binary—it operates along a continuum. For instance, the terms medication and therapy are more semantically related than medication and surgery. Modern NLP systems increasingly rely on word embeddings, which are mathematical representations of words in a continuous vector space (195), These vectors capture semantic relationships based on how words appear in context. In such models, similarity is calculated using vector operations, enabling systems to infer relationships between terms even when they do not share identical surface forms (196). Ontology-based embedding and pre-training approaches aim to improve upon traditional embeddings by integrating structured semantic knowledge. Instead of relying solely on contextual co-occurrence, these methods incorporate hierarchical and relational information from ontologies to guide the learning of vector representations. This can involve embedding the ontology structure itself into vector space or aligning text-derived vectors with ontology-based concept vectors (197) 141 By doing so, the resulting embeddings reflect not only language usage patterns but also curated domain semantics. This combination of ontology-driven structure and distributional semantics supports more accurate entity linking, semantic clustering, and classification, especially in complex biomedical domains where terminology is specialized and ambiguous (198). 5.2.1.6 Relation extraction and modeling Relation extraction is a critical task in biomedical text mining that focuses on identifying and classifying semantic relationships between entities, such as diseases, chemicals, genes, and phenotypes (199). By uncovering how these entities are connected in natural language, relation extraction facilitates knowledge discovery and supports applications like bio-curation (200), drug repurposing (201), and clinical decision-making (202). The goal is not only to recognize entities, but also to define how they relate—e.g., “Drug A treats Disease B”—in structured, machine-readable form (203) Traditional relation extraction methods use linguistic pattern matching, dependency parsing, or machine learning classifiers trained on labeled relations. While effective in narrow domains, these approaches often struggle with generalizability and semantic interpretation, particularly when entity relations are implicit or domain-specific. Ontology-enhanced relation extraction introduces formal semantics to the process. Ontologies provide well-defined relationship types (e.g., is_a, part_of, has_disease_location) along with constraints that govern how entities can validly relate. This allows systems to validate extracted relationships against domain knowledge, improving both precision and interpretability (204). 142 Furthermore, ontologies support logical inference, enabling systems to derive new relationships from known ones based on hierarchical or transitive structures. Figure 5.3 illustrates the use of ontologies in relation extraction. It shows how a sentence such as “Pneumonia is an infection of the lung” can be decomposed into recognized entities and structured relationships. Ontological concepts (e.g., Mondo Disease Ontology (205) (MONDO) terms for disease, Uber Anatomy Ontology (206) (UBERON) for anatomy) are linked, and relations like is_a and has_disease_location are assigned to formalize the meaning of the text. Figure 5.3 An example of semantic relation extraction Concept recognition and relation extraction are applied to the sentence “Pneumonia is an infection of the lung,” resulting in formal entity mappings and ontological relations such as is_a and has_disease_location Pneumonia is an infection of the lung Concept recognition Pneumonia is an infection of the lung disease disease Body part Relation extraction Pneumonia is an infection of the lung is_a has_disease_location pneumonia EFO:0003106 respiratory tract infectious disease MONDO:0024355 lung UBERON:0002048 143 5.3 Methodology This review in this chapter was conducted in accordance with the guidelines for undertaking systematic literature reviews proposed by Kitchenham and Charters (83). The review followed a structured process comprised of planning and scoping, defining inclusion and exclusion criteria, information extraction, quality assessment, and reporting of findings. These steps ensured that the review remained focused, transparent, and reproducible, while also allowing for thematic synthesis across diverse applications of ontology-based biomedical text mining. 5.3.1 planning and scoping Ontologies have been widely used in various biomedical text-mining studies. The research papers examined in this review were investigated based on questions chosen for our study: Q1: What is the source of data which were harnessed for the text mining system? Various data sources within the biomedical domain are available in free-text format. This research question aims to identify the diverse data sources that researchers have utilized in their text mining approaches. Q2: What ontology-based tasks and approaches are used for biomedical text mining? Several key ontology-based approaches are used in biomedical text mining. This research question aims to identify different approaches used for biomedical text mining. Q3: How does domain specificity affect ontology-based text mining techniques? 144 Domain specificity significantly influences ontology-based text mining techniques in several ways, such as conceptual relevance and semantic heterogeneity. This research question aims to investigate how domain specificity affects ontology-based text mining. Q4: Are there any recommendations for effectively constructing and designing ontologies for text-mining purposes? This research question aims to summarize any published recommendations for effectively constructing and designing ontologies for text-mining purposes. 5.3.2 Inclusion criteria The selection of studies for this review was guided by a predefined set of inclusion and exclusion criteria. Only peer-reviewed, English-language primary research articles were considered. Secondary and tertiary sources—such as review papers, surveys, and meta-analyses—were excluded to ensure the focus remained on original contributions. Studies were eligible for inclusion if they:  Presented ontology-based techniques applied to biomedical text mining;  Focused on specific biomedical application areas using clearly defined datasets;  Addressed one or more of the research questions outlined in Section previously. Studies were excluded if they:  Focused solely on general-purpose algorithms or generic text mining tools without applying them to a biomedical context;  Lacked sufficient detail on ontology integration or biomedical text data. 145 As the key objective of this study is to evaluate work detailed in ontology-based text mining research papers, studies detailing techniques in specific biomedical application areas using clearly defined datasets were included, while those describing generic algorithms and text mining tool implementations were excluded, as they do not provide insights into the research questions. The search strategy involved a manual search on Google Scholar and PubMed with keywords “biomedical text (mining or analysis or analytics) and (system and/or application) and (ontologies and/or semantics).” Following an initial screening based on the inclusion and exclusion criteria, 10 studies were selected for review. The selected studies have been assessed based on the research questions defined earlier, the data extracted from each paper available in D.1 supplementary method were as follows: ● Study title and reference of the research paper. ● The data sources which were used. ● Tool name (if applicable). ● Ontology-based approach used. ● Biomedical ontology used in the text mining system. 146 5.4 Results A total of 10 studies were included in this review. The screening and selection process is illustrated in Figure 5.4, following a Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA-style flow diagram). Figure 5.4 PRISMA-style flow diagram illustrating the identification, screening, eligibility assessment, and final inclusion of studies in the review The process began with an initial pool of retrieved records. Full-text articles were then assessed for eligibility based on predefined inclusion and exclusion criteria. The final set of studies included in the review reflects those that met all quality and relevance thresholds. This diagram ensures transparency and reproducibility of the review methodology. 147 The analysis of the selected studies was guided by the four research questions defined during the planning phase of the review. These questions were designed to explore how ontologies have been applied in biomedical text mining, what types of data and tasks are most commonly addressed, and what design considerations influence ontology effectiveness. The results are presented below, organized by research question. Q1: What is the source of data which were harnessed for the text mining system? This review revealed three main sources of data harnessed in the context of leveraging ontologies for biomedical text mining. These sources are described below: 1. Free text documents: including narrative clinical reports and descriptive annotations, were used in studies focused on entity recognition and normalization (156,207,208). These sources were unstructured but rich in biomedical terminology, requiring ontologies to impose conceptual structure. 2. Clinical notes from EHR were featured in studies such as Ontology Based Clinical Information Extraction (OB-CIE) (209), where ontology-based NLP was used to extract structured representations of diagnoses, treatments, and patient attributes. These notes offered a mix of narrative language and clinical jargon, benefiting from semantic disambiguation provided by domain ontologies. 3. Published literature datasets (e.g., PubMed, MEDLINE) were widely used for large-scale extraction of biomedical knowledge. Several studies (210–212) mined abstracts and full-text articles to support tasks such as phenotype–gene association mapping, semantic similarity computation, and cross-resource entity alignment. 148 Q2: What ontology-based techniques and approaches are used for biomedical text mining? The reviewed studies applied ontology integration across multiple text mining tasks. Rather than introducing new methods, most systems enhanced existing NLP pipelines by leveraging structured vocabularies, semantic hierarchies, and formal concept definitions. The most common ontology-based approaches observed are summarized below. Ontology-based named entity recognition: Five studies used ontologies to support named entity recognition, primarily through tools like MetaMap, which maps biomedical terms to the Unified Medical Language System (UMLS) Metathesaurus (213). These studies applied ontological resources to improve entity boundary detection, semantic type assignment, and disambiguation (208–210,212,214) Ontology-based named entity linking: Entity linking was frequently performed using ontology-backed concept identifiers and annotation properties (e.g., labels, definitions). For instance, MetaMap was also used to map recognized entities to canonical UMLS concepts, supporting both disambiguation and normalization (215) (90). Ontology-based scoring and ranking: Several studies used ontologies for concept similarity computation, particularly via hierarchical structures and taxonomic relations. For example, Kafkas and Hoehndorf (210) applied Resnik's semantic similarity measure to compare phenotypic terms in the Human Phenotype Ontology (216) enabling the ranking of gene–disease associations. 149 Ontology-based relation extraction: Ontology-driven relation extraction was implemented in tools like clinical Text Analysis and Knowledge Extraction System (cTAKES) (214), which used ontological semantic types to identify and interpret relationships between recognized biomedical entities. Ontology-based text normalization: A smaller number of systems incorporated formal description logic–based ontologies (e.g., OWL DL) to support text normalization and semantic inference. Witte et al. (217) proposed one such system that used ontological reasoning to annotate and enrich biomedical text, supporting complex querying and concept classification Q3: How does domain specificity affect ontology-based text mining techniques? Domain specificity plays a critical role in enhancing the performance of ontology-based text mining techniques in biomedical applications. By leveraging the structured and hierarchically organized knowledge within domain-specific ontologies, such as the Human Phenotype Ontology (HPO) and Mammalian Phenotype Ontology (MP), text mining systems can improve the recognition, normalization, and semantic understanding of complex biomedical entities. Kafkas and Hoehndorf demonstrated that incorporating ontological background knowledge enables the system to propagate phenotype mentions across hierarchical relationships, such that subclass mentions (e.g., “brain inflammation”) implicitly indicate parent class mentions (e.g., “CNS inflammation”), thereby improving robustness and recall even when exact terms are absent from the text (218). This semantic propagation is further facilitated by the axiomatic structure of ontologies, allowing systems to detect implicit relations and support downstream 150 tasks such as candidate gene prediction. Similarly, Groza et al. showed that domain-specific ontologies help resolve ambiguity in phenotype expression—particularly when dealing with metaphorical terms (e.g., “hitchhiker thumb”), anatomical nesting, or abbreviations common in clinical texts (212). These challenges necessitate fine-grained domain modeling; which generic NLP tools often cannot accommodate. Moreover, domain specificity enables targeted natural language processing pipelines. Systems such as cTAKES use machine learning models trained specifically on clinical narratives, achieving higher accuracy in sentence detection, tokenization, and named entity recognition by exploiting clinical domain constraints (214). MetaMap, another domain-specific system, maps biomedical text to the UMLS Metathesaurus using linguistic and lexical resources tailored to biomedical concepts, demonstrating the importance of semantic type constraints and synonym expansion in improving mapping fidelity (207). Domain-specific annotation tools like the Open Biomedical Annotator (OBA) further illustrate how biomedical terminologies, when semantically expanded, improve recall by recognizing hierarchical and sibling relationships across ontologies—for example, identifying that "melanoma" is a type of "skin tumor" and "neoplasm" through transitive is-a relations (156). However, the benefits of domain specificity come with increased complexity. For example, Groza et al. noted that phenotype descriptions often deviate from canonical forms, making it difficult to identify exact concept boundaries without expert-curated ontologies and corpora (212) The DiseaSEA system confirms this by showing that domain-specific rule sets and dependency parsing are needed to accurately extract disease–symptom relationships from unstructured text (208). Overall, while domain specificity 151 introduces challenges in handling expression variability and ontology integration, it remains indispensable for achieving precision, recall, and contextual accuracy in biomedical text mining. Q4: Are there any recommendations for effectively constructing and designing ontologies for text-mining purposes? Effective construction and design of ontologies for text mining in the biomedical domain require several considerations to enhance performance, accuracy, and interoperability. First, ontologies should be axiomatically rich and hierarchically structured, allowing inferencing over subclass relationships; for example, if “brain inflammation” is a subclass of “CNS inflammation,” then any mention of the former should imply the latter to support more complete extraction and reasoning across texts (210) This principle of semantic propagation significantly boosts coverage and recall, especially when the ontology is used as background knowledge to infer indirect annotations. Additionally, semantic similarity calculations based on these hierarchical relationships are essential for robust phenotype-based applications such as gene-disease prediction. Second, ontologies designed for text mining should include rich lexical variants—such as synonyms, abbreviations, and morphological forms—to ensure successful concept recognition across varied linguistic expressions. For instance, the OBA uses extensive dictionaries built from public ontologies like UMLS and BioPortal, emphasizing that concept labels should cover multiple surface forms for each class to facilitate string-matching techniques. These dictionaries should ideally be paired with semantic expansion modules that traverse is_a hierarchies to annotate parent and sibling terms, expanding coverage without losing specificity (156). The 152 semantic expansion step is particularly crucial for recognizing implicit relations and enabling flexible querying of annotated datasets. Third, customizability and modularity in ontology services are highly recommended. The OBA framework demonstrates that ontology-based tools must allow users to configure the set of ontologies and parameters used for annotation depending on the application domain—for example, SNOMED-CT for diseases, or the Mammalian Phenotype Ontology for genetics (156) This modularity ensures relevance and minimizes noise from unrelated concepts. Lastly, for ontologies to be effective in text mining, they should be developed with attention to computational efficiency and integration support. The ability to preload hierarchies and keep dictionaries in memory, as done in OBA, drastically reduces processing times and facilitates real-time or large-scale analysis workflows. As noted in prior work, the lack of standardization across ontologies, overlapping concepts, and inconsistent formats also hinders annotation; thus, adopting interoperable standards (e.g., OBO format) and providing APIs for access and integration are crucial for usability in automated pipelines (156). 5.5 Discussion This review highlights the importance of ontologies in enhancing the effectiveness of biomedical text mining systems. Across various computational tasks—from natural language processing and named entity recognition to information extraction and relation modeling—ontologies provide structured semantic frameworks that improve accuracy, interpretability, and interoperability of extracted information. Their ability to formalize domain knowledge using well-defined 153 hierarchies and relationships makes them especially valuable in a field characterized by complex and often ambiguous terminology. One of the clearest advantages of ontology integration is observed in named entity recognition. Traditional approaches often rely on statistical models that perform well when sufficient training data is available but struggle with synonymy, abbreviation, and polysemy—phenomena that are common in biomedical literature. Ontology-based named entity recognition systems overcome these challenges by linking textual mentions to structured vocabularies such as UMLS, GO, or the HPO. This linkage not only supports improved disambiguation and normalization but also facilitates the application of domain and range constraints that guide entity recognition with higher semantic precision. As shown in tools like MetaMap and OBA, the inclusion of ontologies leads to more accurate concept tagging and classification. Another area where ontologies have significantly advanced text mining is relation extraction. Biomedical texts often contain implicit or variable expressions of semantic relationships, such as "Drug A treats Disease B" or "Gene X is associated with Phenotype Y." Ontologies enhance the extraction of such relations by providing a predefined set of semantic properties and relationship types (e.g., is_a, has_location, associated_with) that can be used to classify or validate the relationships identified. In addition to improving precision, these predefined structures enable logical inference—allowing the system to deduce new relationships that were not explicitly mentioned but follow logically from the ontology’s axioms. Text classification also benefits from ontology-guided enrichment. By organizing terms into semantically meaningful categories and taxonomies, ontologies allow classification algorithms to 154 reason over concept neighborhoods, generalize from sparse data, and recognize abstract categories. Unlike surface-level keyword matching, ontology-enhanced classification considers the broader conceptual context of terms, improving performance in tasks such as disease classification, treatment categorization, and literature triage. Even in cases where labeled training data is limited, the inherent structure and relationships in ontologies enable meaningful categorization by leveraging semantic similarity scores and conceptual distance metrics. Additionally, Ontology-Based Information Extraction (OBIE) has emerged as a powerful technique that leverages domain ontologies to guide the identification and structuring of information from unstructured text. OBIE systems utilize ontologies both as annotation schemas and reasoning engines, converting narrative documents into structured data entries. These systems are especially beneficial in clinical informatics, where physician notes and EHR narratives can be transformed into semantically annotated representations of symptoms, diagnoses, and treatments. This structured output supports downstream applications such as decision support systems, clinical dashboards, and population health analysis. Finally, ontology-based approaches have been instrumental in the development of pre-trained embeddings and semantic models. Embedding ontological relationships into vector space representations has enabled the combination of distributional semantics (based on co-occurrence patterns) with curated domain semantics. This hybrid approach enhances the quality of concept representations used in entity linking and classification, particularly in domains where terminology is dense, overlapping, or highly specialized. 155 In summary, the integration of ontologies into biomedical text mining pipelines has proven highly effective across a range of tasks. These systems not only benefit from the semantic richness and inferential capabilities of ontologies but also support consistent annotation, data harmonization, and enhanced interoperability. As biomedical knowledge continues to expand, the role of ontologies in structuring and interpreting that knowledge through text mining will become increasingly central to both research and clinical applications. In conclusion, Ontology-based biomedical text mining has demonstrated significant promise in addressing the challenges associated with extracting structured knowledge from unstructured and semi-structured biomedical text. The integration of ontologies into text mining systems has enabled improved entity recognition, relation extraction, classification, and semantic enrichment by grounding textual content in a formal conceptual framework. By leveraging the structured knowledge encoded in biomedical ontologies, such systems are able to enhance semantic interpretation, reduce ambiguity, and support more accurate and interoperable outputs. This review has shown that ontologies play a central role in enabling consistent annotation, cross-domain integration, and inferencing across biomedical texts. Their ability to represent complex domain knowledge—including hierarchical relationships, standardized vocabularies, and domain-specific properties—has made them essential in modern biomedical informatics. Applications ranging from candidate gene identification to semantic search, disease modeling, and clinical decision support have benefited from ontology-enhanced text mining approaches. As biomedical data continues to grow in scale and complexity, future work should focus on developing scalable, modular ontologies tailored for text mining purposes, improving tools for 156 ontology integration, and advancing hybrid approaches that combine symbolic reasoning with machine learning. Continued efforts toward ontology standardization, community-driven development, and alignment with evolving biomedical terminology will be critical to fully realizing the potential of semantic technologies in biomedical text mining. Looking ahead, the growing use of Large Language Models (LLMs), such as BioGPT and PubMedBERT (219), introduces new opportunities for biomedical text mining. These models have shown strong performance in extracting and summarizing information from biomedical literature, even with limited task-specific training. However, while LLMs are fluent and flexible, they often produce outputs that lack the semantic precision, domain consistency, or use of standardized terminology that ontologies are designed to enforce. Ontologies can help address these challenges by grounding LLM outputs in structured and well-defined biomedical knowledge. Integrating ontologies into LLM workflows—whether through prompting, post-processing, or training—can improve the reliability and semantic accuracy of extracted information. Future work should explore how ontologies and LLMs can be combined to support more accurate, interpretable, and domain-aware biomedical text mining systems. Recent studies have begun to explore this intersection between ontologies and LLMs in practical contexts. For instance, Kokash et al. proposed an ontology- and LLM-based data harmonization framework for federated healthcare learning, demonstrating improved data interoperability and mapping precision through ontology-guided reasoning (220). Likewise, Olasunkanmi et al. introduced RELATE, a framework aligning relations extracted by LLMs from biomedical literature with ontology predicates such as those in ChemProt and the Biolink Model, enhancing 157 semantic accuracy and integration into knowledge graphs (221). These developments illustrate that ontology-integrated LLM approaches represent a rapidly evolving research direction, reaffirming the continued relevance and adaptability of ontology-based methods in biomedical text mining. Future work should explore how ontologies and LLMs can be combined to support more accurate, interpretable, and domain-aware biomedical text mining systems. 158 Chapter 6: Discussion, conclusion, and future directions 6.1 Outstanding Questions and Future Directions The work described in this thesis opens up a number of promising opportunities for future exploration to further investigate what semantic web technologies offer to improve the bio-banking and biomedical domain. Future directions for investigation include the following projects: 6.1.1 Fine-grained healthcare data access control and cyber threats prevention using graph databases project (Approved grant) Among the most apparent risks to healthcare participant protection is the potential for unauthorized information access. Accordingly, access restriction serves as a key countermeasure (222,223), making it essential for data custodians to control access to sensitive local variables, such as PII. Access control, broadly defined, is the framework that guides how data access decisions are made and enforced. In recent years, semantic web technologies and languages have been proposed as powerful tools to encode and enforce access control policies for sensitive biomedical data (224). These techniques leverage RDF and SPARQL to represent and enforce policies in a machine-interpretable format. As explained in 4.3, Research into RDF access control has produced several models, which can be categorized into three main approaches (225); Named graph-based access control, Rule-based access control, and Ontology-based access control. This project builds directly on the federated querying architecture and access control considerations presented in Chapter 4 of this thesis, where the challenges of secure, decentralized data sharing were first explored in detail. Each approach operates at a different level of 159 granularity, typically offering binary access decisions. However, one major limitation of current access control systems is that they do not support abductive reasoning—a type of reasoning that helps figure out what needs to happen to meet a certain goal. Without this, it’s difficult to predict how a new access policy might affect data access or to figure out what kind of permissions a user would need to get the results they want. While some general-purpose policy tools can handle this kind of reasoning, it hasn’t yet been applied to access control systems for RDF and graph-based data. In this project, I will implement ontology-based access control for a decentralized environment, enabling data consumers to interact with resources using defined semantic relations. Users, entities, and relationships will be identified using HTTP URIs, in alignment with linked data principles. This design ensures fine-grained, interoperable, and traceable access control over distributed biobank resources. Principle Investigator: Dalia Alghamdi IRB number: 23-144 Grant information: Approved from King Fahad Medical City Grant department RFA 023-020 grant award – (74,199 SAR) Start/End date: July 2023- December 2025 160 6.1.2 Leveraging ontologies for variant classification and decision support system (Approved grant). The The growing affordability and adoption of DNA sequencing in clinical settings have transformed pathology laboratories but also introduced new challenges in variant analysis and reporting. One persistent problem is the misclassification of genetic variants, which can have serious consequences for diagnosis and treatment. Accurate classification requires deep domain knowledge about gene structure and function, prior variant evidence, and disease associations (226). To support clinicians, several open-access and commercial resources have emerged, such as OncoKB(227), CIViC (228), My Cancer Genome(229), and PMKB . Despite their utility, studies such as Katsoulakis et al.,(230) 2020 and Wagner et al.,(231) 2020 have shown considerable variability among these platforms in the guidelines they adopt—ranging from the AMP and ACMG to lab-specific curation rules. Some provide stringent interpretations, while others leave the burden of prioritization to clinicians, increasing the risk of inconsistency. Similarly, variant filtration is often customized at the lab level, further complicating standardization (232) (Koopman et al., 2020). To address these challenges, this project proposes a semantic web-based Clinical Decision Support System (CDSS) that uses biomedical ontologies—primarily NGBO—to structure and encode variant classification knowledge. This application was developed based on insights and recommendations derived from the systematic review presented in Chapter 5, which highlighted the potential of combining ontologies with natural language processing and text mining to 161 improve the accuracy, transparency, and scalability of knowledge extraction for variant interpretation. This approach builds upon the ontology design work presented in Chapter 3, where NGBO was developed as a foundation for representing structured biomedical knowledge and supporting interoperability. The CDSS will function as a reasoning system that integrates patient data, variant metadata, and biomedical knowledge to assist clinicians in interpreting somatic and germline variants. Ontologies serve as an ideal framework for this application for several reasons. First, they offer formal, machine-readable representations of biomedical knowledge and terminology. NGBO, for instance can be extended to annotate variants using resources like ClinVar and scientific literature from PubMed. Ontology-based rules can enforce standardized interpretations, reducing the number of variants of unknown or conflicting significance. Second, ontologies support semantic reasoning, enabling the CDSS to derive logical inferences based on established relationships. For example, if a variant is linked to a known gene-disease association in NGBO and appears in a curated ClinVar record, it can be automatically prioritized and flagged. This formal structure also facilitates the reuse of decision support rules and knowledge across systems (233). Finally, the CDSS will leverage text mining and NLP to extract relevant knowledge from biomedical publications. This is crucial for capturing the most recent evidence in a rapidly evolving field. Tools like MedCAT and MetaMap have demonstrated the power of combining 162 ontologies with text mining to annotate clinical records and research literature. The use of RDF and OWL enables integration of unstructured and structured data into a unified knowledge graph. By combining ontologies, curated databases, and text mining, this CDSS will support robust, explainable, and reproducible variant classification for clinical genomics. The system will be built using NGBO as the core knowledge model and will align with FAIR principles, ensuring that the resources it generates are Findable, Accessible, Interoperable, and Reusable. Principle Investigator: Dalia Alghamdi IRB number: 23-255 Grant information: Approved from King Fahad Medical City Grant department RFA 023-031 grant award – (194,000 SAR) Start/End date: November 2023- November 2025 6.2 Discussion and Conclusion In conclusion, the proposed plan of leveraging semantic web technologies (e.g., SPARQL, RDF, ontologies) to build an application in order to improve the querying process and integrate biobank specimens and specimen-derivative data has been completed. This project has developed from the following resources: (i) an openly published and well-curated ontology, NGBO, consisting of 1,643 classes (192 defined within NGBO), 30 individuals, 32 object properties, and 1,942 logical axioms and relations. NGBO complies with W3C and OBO Foundry standards, making it interoperable, extensible, and accessible for collaborative development. It addresses persistent challenges such 163 as the lack of standardized metadata formats and the absence of minimum information requirements in omics biobanking, bridging a critical gap in the domain. (ii) A publicly available web application that enables users to import, query, and retrieve data using SPARQL and APIs over RDF triples stored in a graph database. This application overcomes the challenge of distributed biobanking datasets by enabling unified access through semantic federation, effectively providing a single, integrated interface across disparate data sources. Semantic web technologies and data federation approaches together offer a sustainable infrastructure for managing complex, distributed metadata. This work demonstrates that a federated model allows for distributed curation, helping keep metadata close to the domain experts who generate and maintain it, thereby improving accuracy and relevance. Unlike centralized systems, which risk becoming outdated or disconnected from contributors, this federated architecture avoids a single point of failure and enables greater resilience. Additionally, registries in a federated ecosystem can adapt to the needs of specific sub-communities, allowing innovation in how data is presented and discovered. However, formal standards for how contextual data should be formatted and shared remain inconsistent, limiting interoperability across platforms and institutions. Overall, this thesis addresses an urgent and growing challenge in biomedical research: the need for well-curated, interoperable knowledge representation as biobank data continues to increase in volume, diversity, and complexity. The tools and approaches developed in this work offer a model for how semantic web technologies can meet that need. Projects like this will continue to be valuable assets for biobanks in both academic and industry settings. The field of omics 164 knowledge representation has grown significantly even within the timeframe of this research. As this field continues to expand, the need for robust data integration strategies will become even more pressing. While this work has limitations, it demonstrates that semantic web technologies provide a strong foundation for scalable and interoperable data federation in the biobanking domain. Some areas lie beyond the scope of this thesis, such as the need for automated processes that can transform relational databases (including both schema and instance data) into property graph databases, enabling real-time synchronization and full semantic integration. Additional enhancements to this work could follow two important directions. First, NGBO could be extended by integrating additional domain-specific ontologies relevant to biobanking, such as those describing clinical phenotypes (e.g., disease progression, symptoms), imaging data (e.g., MRI or CT scan metadata), or environmental exposures (e.g., pollutant levels, lifestyle factors). These expansions would enrich NGBO’s semantic coverage, allowing it to better represent the multifaceted data associated with biobank specimens and their contexts, ultimately supporting more complex queries and interdisciplinary research use cases. Looking more broadly, it is anticipated that emerging open-source and commercial federated data integration platforms—such as Microsoft Fabric—and advances in artificial intelligence will also contribute to the evolution of biobank data management. Although these platforms have not been widely adopted in biobanking to date, they offer capabilities such as integrated metadata cataloging, schema mapping, and data history tracking, which can help automate aspects of data standardization and support more robust cohort tracking. These systems are expected to complement ontology-driven approaches by providing scalable infrastructure for harmonization across heterogeneous data sources. Second, further efforts are needed to promote the sharing and reuse of biobanking data 165 across institutions and research domains. This includes encouraging adoption of NGBO and similar semantic standards in other biobank infrastructures, improving interoperability between repositories, and supporting frameworks for secure and ethical data access. By enabling broader and more consistent reuse of biobank data, these efforts can significantly amplify the scientific value of existing resources. With the continuing advancements of this work, a fully semantic web-based federation with real-time data synchronization for biobanks is feasible. Significant extensions to the web application would involve implementing more advanced features from traceability of specimens and data, even after being deported from the biobank. Additionally, real-time synchronization with traditional EHR systems is a pre-request to encourage stakeholders to become part of the federations, as convincing them to change the traditional data management systems they have been using might be hard. 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Competency question 1: Search for disease x (adenocarcinoma specimen) omics-derived data (DNA sequence data) across all available repositories. Competency question 2: Search for disease x (adenocarcinoma specimen) omics-derived data y (DNA sequence data) available in repositories a, b (King Fahad Medical City biobank & King Faisal Specialist Hospital) with a mutation in gene z (KRAS mutation). 187 Competency question 3: Data format x (fasta files) for omics-derived data y (DNA sequencing data) for disease z (autism) filtered by life stage (pediatric patients) available in repositories a (King Fahad Medical City) Competency question 4: Number of data formats x (fasta files) for omics-derived data y (DNA sequencing data) for disease z (autism) filter by life stage (pediatric patients) 188 Competency question 5: Search for omics-derived data for disease x (bladder cancer) and subset it by smoking history (current reformed smoker for less than 15 years’ history), (current reformed smoker for more than 15 years’ history). Competency question 6: Search for omics-derived data (DNA sequencing data) x with mutation y (FGFR3) and z (PIK3CA) 189 B.2 Supplementary method – Publications analyzed to identify contextual data elements for NGBO specifications. Title Authors PMID Publication Year The State of Cloud-Based Biospecimen and Biobank Data Management Tools Shonali Paul, Aditi Gade, Sumani Mallipeddi 28300421 2017 Metabolomics technology and bioinformatics for precision medicine Rajeev K Azad, Vladimir Shulaev 29304189 2018 The Implementation of an Integrated Management System at Qatar Biobank Linda Hannigan, Ghada Deyab, Asmaa Al Thani, Ajayeb Al Marri, Nahla Afifi 31833808 2019 Biobanks for life sciences and personalized medicine: importance of standardization, biosafety, biosecurity, and data management Heimo Müller, Georges Dagher, Martina Loibner, Cornelia Stumptner, Penelope Kungl, Kurt Zatloukal 31896493 2019 A review of brain biorepository management and operations H Ronald Zielke, Deborah C Mash 29496157 2018 ukbtools: An R package to manage and query UK Biobank data Ken B Hanscombe, Jonathan R I Coleman, Matthew Traylor, Cathryn M Lewis 31150407 2019 Aspects of Modern Biobank Activity – Comprehensive Review Wiktor Paskal, Adriana M Paskal, Tomasz Dębski, Maciej Gryziak, Janusz Jaworowski 29728978 2018 Data Integration in Poplar: ‘Omics Layers and Integration Strategies Deborah Weighill, Timothy J Tschaplinski, Gerald A Tuskan, Daniel Jacobson 31608114 2019 Integration of Omics Data to Identify Cancer-Related MicroRNA Luciano Cascione 30963490 2019 190 Challenges in the Integration of Omics and Non-Omics Data Evangelina López de Maturana, Lola Alonso, Pablo Alarcón, Isabel Adoración Martín-Antoniano, Silvia Pineda, Lucas Piorno, M Luz Calle, Núria Malats 30897838 2019 A Selective Review of Multi-Level Omics Data Integration Using Variable Selection Cen Wu, Fei Zhou, Jie Ren, Xiaoxi Li, Yu Jiang, Shuangge Ma 30669303 2019 Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data Andreas Holzinger, Benjamin Haibe-Kains, Igor Jurisica 31203421 2019 Biobank Quality Management in the BBMRI.be Network Loes Linsen, Veronique T'Joen, Catherine Van Der Straeten, Kristel Van Landuyt, Etienne Marbaix, Sofie Bekaert, Nadine Ectors 31294024 2019 Returning Results in Biobank Research: Global Trends and Solutions Eva De Clercq, Jane Kaye, Susan M Wolf, Barbara A Koenig, Bernice S Elger 28146646 2017 BioInfra.Prot: A comprehensive proteomics workflow including data standardization, protein inference, expression analysis and data publication Michael Turewicz, Michael Kohl, Maike Ahrens, Gerhard Mayer, Julian Uszkoreit, Wael Naboulsi, Thilo Bracht, Dominik A Megger, Barbara Sitek, Katrin Marcus, Martin Eisenacher 28606611 2017 191 B.3 Supplementary method - An example of ontologised terms Label Definition Example of usage Data availability Examples of how this data item is store before (if available) mini model 16s RNA sequence data RNA sequence data which is about the 16s gene of one or more organisms. 16s RNA sequence data is used to identify bacteria at the species level in order to differentiate between closely related species. Electronic records High-throughput sequencing data of amplified 16s rRNA genes / 16S rRNA gene sequencing ACMG guidelines for classification of variants A protocol which describes the guidance for the interpretation of sequence variants suggested by The American College of Medical Genetics and Genomics (ACMG). (ACMG-AMP) system for variant classification is score based with five classes: benign, likely benign, variant of unknown significance (VUS), likely pathogenic, and pathogenic in silos (textual entities as part of clinical reports / SOPs) Example 1) We consider this variant to be likely pathogenic (class 2) according to recommendations ACMG Example 2) These include pathogenic or likely pathogenic variants in genes described in the ACMG Recommendations for Reporting of Incidental Findings in Clinical Exome and Genome Sequencing, Availability of other specimen from the same donor A data item which represents the availability of other specimen from the same donor To specify the availability of availability of other specimens from the same donor, vital information for research (Such as cancer research). Information not available 192 BWA assembly process Bioinformatics analysis which performs gapped alignment for single-end reads, supports paired- end mapping, generates mapping quality, and gives multiple hits if required BWA assembly is the process of aligning sequencing reads against a large reference genome (e.g. human genome). in silos (SOPs and Bioinformatics pipelines) Commonly used aligners include the Burrows-Wheeler Aligner (BWA) [53] and Bowtie2 [54]. Ion Torrent™ also have their own customized aligner specifically for working on data generated from their platform. Within alignment files the mapping quality score (i.e., the likelihood of a read mapping to multiple locations in the genome) is recorded for each read, in addition to their mapped coordinates. Burrows-Wheeler Aligner algorithm An algorithm which performs mapping of low-divergent sequences against a large reference genome, such as the human genome. in silos (SOPs and Bioinformatics pipelines) Commonly used aligners include the Burrows-Wheeler Aligner (BWA) [53] and Bowtie2 [54]. Ion Torrent™ also have their own customized aligner specifically for working on data generated from their platform. Within alignment files the mapping quality score (i.e., the likelihood of a read mapping to multiple locations in the genome) is recorded for each read, in addition to their mapped coordinates. Clinical variant interpretation A data item representing a variant’ s significance in relation to a disease or phenotype. Clinical variant interpretation data is a representation of computational assessment of clinical impacts of somatic variants. Textual entities as part of clinical reports / in silos as part of SOP's The ACMG guidelines should be used for classification and interpretation of germline variants in inherited disorders. DNA extraction assay kit An assay kit used for the technique of isolating DNA in a biological sample A typical DNA extraction kit uses some form of alkaline lysis followed by incubation with a DNA binding matrix, washing, and then elution / Information on cDNA and library preparation methods may affect the outcome and interpretation of met transcriptomic data. Textual entities as part of clinical reports / in silos as part of SOP's DNA was extracted using both the QIAamp DNA Investigator Kit and the phenol-chloroform method 193 DNA library A process material which contains DNA fragments that represent the entire genome of an organism. A DNA library is a collection of DNA fragments that have been cloned into vectors so that researchers can identify and isolate the DNA fragments that interest them for further study / Information on cDNA and library preparation methods may affect the outcome and interpretation of met transcriptomic data. Textual entities as part of clinical reports / in silos as part of SOP's DNA using the Agilent SureSelect Target Enrichment workflow to capture regions of interest from a DNA fragment library. DNA processed specimen A processed specimen which is the output of preparing a DNA sample for further analysis. DNA processed specimen generated through the use of a technique known as Polymerase Chain Reaction (PCR) Textual entities as part of clinical reports / in silos as part of SOP's HUGO gene nomenclature committee gene name A biological database that contains gene nomenclature created by the HUGO Gene Nomenclature Committee (HGNC) that sets the standards for human gene nomenclature. Use Human Genome Organization (HUGO) Gene Nomenclature, italicized, with all letters in uppercase (e.g., NLGN1, for neuroligin1), As part of clinical report SIFT score impact Data item which represents a normalized probability of observing the new amino acid at that position and ranges from 0 to 1. SIFT scores classified as deleterious (< 0.05), degree of impact on the consensus region of each polymorphism Textual entities as part of the bioinformatics processes and thresholds applied for prioritizing and identifying causal or candidate variants or genes Sift 0.98 / SIFT (0.06) / a SIFT (Sorting Intolerant from Tolerant) score of 0.02 Adapter ligation process A material processing process which attaches synthetic oligonucleotides of a known sequence to one or both ends of cDNA fragments Ligation technology is used to construct NGS libraries for sequencing. The process uses an enzyme to connect specialized adapters to both ends of DNA fragments / Information on methods and reagents used for adapter ligation is crucial. Usually these adaptors get sequenced, and have in silos (textual entities as part of clinical reports / SOPs) an enrichment strategy that uses targeted cleavage of chromosomal DNA with Cas9 to ligate adapters for nanopore sequencing 194 already been removed from the reads. But sometimes bits of adaptors are left behind. These need to be removed from the reads. adapter ligation protocol A protocol which describes the process of attaching synthetic oligonucleotides with known sequences to both or either ends of complementary DNA (cDNA) fragments as a preparation for sequencing After completion of the End Repair/dA-Tailing program add 1.0 μl of adaptor (or vector if generating an expression library), to the reaction tube. (Adaptors/vectors are not provided. Please use adaptors/vectors appropriate to specific applications). Add 1.0 μl of Quick T4 DNA Ligase. Vortex briefly to mix, followed by a in silos (textual entities as part of clinical reports / SOPs) https://international.neb. com/protocols/0001/01/01/nebnext- adaptor-ligation-e6080 allele specific PCR protocol A protocol which describes the process of Allele-specific polymerase chain reaction (PCR) Allele-specific polymerase chain reaction (ASPCR) is an application of the polymerase chain reaction (PCR) that permits the direct detection of any point mutation in human DNA by analyzing the PCR products in an ethidium bromide-stained agarose or polyacrylamide gel. in silos (textual entities as part of clinical reports / SOPs) allele specific PCR A validation of known variant assay which permits the direct detection of any point mutation in human DNA by analyzing the PCR products in an ethidium bromide-stained agarose or polyacrylamide gel Allele-specific polymerase chain reaction (ASPCR) is an application of the polymerase chain reaction (PCR) that permits the direct detection of any point mutation in human DNA by analyzing the PCR products in an ethidium bromide-stained agarose or polyacrylamide gel. in silos (textual entities as part of clinical reports / SOPs) assay kit version A version number of the assay kit. in silos (textual entities as part of clinical reports / SOPs) 195 assay kit A processed material which contains a set of articles or equipment needed for a specific purpose. DNA Purification Kit in silos (textual entities as part of clinical reports / SOPs) DNA was extracted using both the QIAamp DNA Investigator Kit and the phenol-chloroform method benign variant A clinical variant interpretation that represents a sequence variant previously classified or reported as a recognized neutral variant Electronic records / in silos (textual entities as part of clinical reports / SOPs) bioinformatics analysis execution date A time measurement datum of the bioinformatics analysis execution date. Electronic records / in silos (textual entities as part of clinical reports / SOPs) bioinformatics pipeline A software pipeline that is composed of a wide array of software algorithms to process biological raw data. in silos (SOPs and Bioinformatics pipelines) Paired-end reads are aligned to the NCBI reference sequence (GRCh37) using the Burrows-Wheeler Aligner (BWA), and variant calls are made using the Genomic Analysis Tool Kit (GATK). Variants are filtered to identify: (1) variants with a minor allele frequency ≤5% in NHLBI Exome Sequencing Project (http://evs. gs.washington.edu/EVS/) in the patient- specific phenotype-driven gene list (see supplement); (2) variants classified as disease causing mutations in public databases; and (3) predicted loss-of-function variants with a minor allele 196 frequency ≤1%. bioinformatics software name Information about software includes software name in silos (SOPs and Bioinformatics pipelines) - textual entity QIAGEN CLC Genomics workbench bioinformatics software parameter name Information about software includes information on software parameter names. biological database A database which represents libraries of life sciences information, collected from scientific experiments, published literature, high-throughput experiment technology, and computational analysis. Electronic records / in silos (textual entities as part of clinical reports / SOPs) Each variant is evaluated based on the available information from the following: databases (including HGMD, ClinVar, LSDBs, NHLBI Exome Sequencing Project, 1000 Genomes, and dbSNP), 197 data file URL A URL at which the data file file is stored. e.g. \\server01\kfmc\path Electronic records e.g. \\server01\kfmc\path data file format Information content entity that is a standard way that information is encoded for storage in a computer file. The file format specifies how the information is encoded, which application should be used, or can the data be properly parsed. Additionally It specifies how the bits have been used to encode the information. in silos (SOPs and Bioinformatics pipelines) Raw reads (Fasta) / Raw data (Fasta) / data transfer agreement A document information content entity established between organizations that governs the transfer of one or more data sets from the owner/provider to a third party a legal document that lays out the terms and conditions of sending or receiving personal data to another jurisdiction or organization. in silos (SOPs and Bioinformatics pipelines) - textual entity data transfer protocol A protocol plan specification which describes the process of data transfer between two entities Records of security parameters and protocols for NGS data transmission and storage locations, including encryption, control of physical and virtual access to data, system backups and redundancy textual entities in silos as part of SOP's 198 detected gene mutation A conclusion based on data about a permanent change in the DNA sequence that makes up a gene APC gene mutation Electronic records / in silos (textual entities as part of clinical reports / SOPs) APC gene mutation expression quantitative trait loci A genomic feature location which represent the expression quantitative trait loci are genomic loci that explain all or a fraction of variation in expression levels of mRNAs An eQTL is a locus that explains a fraction of the genetic variance of a gene expression phenotype. Electronic records / in silos (textual entities as part of clinical reports / SOPs) Trait SNP Chr Position A1 A2 c2 p Value Probe ID Transcript Gene Transcript Chr eQTL Glu rs1335051 1 107,460,385 G T 9.78 1.8 3 10 3 ILMN_2399463 VAV3 1 cis Glu rs2303565 2 219,253,553 T C 9.33 2.3 3 10 3 ILMN_1704985 CYP27A1 2 cis bioinformatics software parameter value Information about software includes information on software parameter value. in silos (SOPs and Bioinformatics pipelines) 0.4 199 Appendix C Chapter 4 supplementary material C.1 Supplementary method - Web application search questionnaire. Section 1: Competency Questions 1. Search for disease x (adenocarcinoma specimen) omics-derived data (DNA sequence data) across all available repositories. 2. Search for disease x (adenocarcinoma specimen) omics-derived data y (DNA sequence data) available in repositories a, b (King Fahad Medical City biobank & King Faisal Specialist Hospital) with a mutation in gene z (KRAS mutation). 3. Search for Data format x (fasta files) for omics-derived data y (DNA sequencing data) for disease z (autism) filtered by life stage (pediatric patients) available in repositories a (King Fahad Medical City) 4. Number of data formats x (fasta files) for omics-derived data y (DNA sequencing data) for disease z (autism) filter by life stage (pediatric patients) 5. Search for sequencing kit used to generate “specific sample-derived data”. 6. Search for reference genome information. Section 2: User experience questions 1. Would you recommend the web application to your colleagues? 2. Do you think that this application will meet your research purposes? 3. Would you suggest any improvements? 200 C.2 Supplementary method - SPARQL query example @app.route('/filterbybiobank') def filter_by_biobank(): sparql.setQuery(prefixquery + """ SELECT ?bloodspecimenlabel ?biobanklabel ?libraryPreprationlabel ?LibraryPreprationProtocollabel ?LibraryPreprationKitlabel ?Sequencingdevicelabel ?availableSequencingDatalabel ?FileFormatlabel WHERE{ ?bloodspecimen a obo:OBI_0000655 . ?bloodspecimen rdfs:label ?bloodspecimenlabel . ?biobank a obo:OMIABIS_0000000 . ?biobank rdfs:label ?biobanklabel . ?libraryPrepration a obo:OBI_0000711 . ?libraryPrepration rdfs:label ?libraryPreprationlabel . ?LibraryPreprationProtocol a ngbo:NGBO_6000276 . ?LibraryPreprationProtocol rdfs:label ?LibraryPreprationProtocollabel . ?LibraryPreprationKit a ngbo:NGBO_6000255 . ?LibraryPreprationKit rdfs:label ?LibraryPreprationKitlabel . ?Sequencingdevice a obo:OBI_0400103 . ?Sequencingdevice rdfs:label ?Sequencingdevicelabel . ?availableSequencingData a obo:OBI_0001573 . ?availableSequencingData rdfs:label ?availableSequencingDatalabel . ?FileFormat a ngbo:NGBO_6000003 . ?FileFormat rdfs:label ?FileFormatlabel . ?bloodspecimen obo:BFO_0000050 ?biobank . ?libraryPrepration obo:OBI_0000293 ?bloodspecimen . ?libraryPrepration obo:STATO_0000102 ?LibraryPreprationProtocol . ?libraryPrepration obo:OBI_0000293 ?LibraryPreprationKit . ?availableSequencingData obo:IAO_0000136 ?bloodspecimen . ?FileFormat obo:IAO_0000136 ?availableSequencingData . } """) 201 Appendix D Chapter 5 supplementary material D.1 Supplementary method - Summery on ontology-bases biomedical text mining related papers. Paper title Data source Tool name Ontology-based approach Used ontology The Open Biomedical Annotator (156) Free-text (up to 500 words) OBA annotator (1) Named entity recognition (2) Named entity linking (3) Scoring and ranking All the (English) ontologies in UMLS (more than 94) and a subset of the NCBO BioPortal ontologies (207) Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program (210) Free text documents MetaMap Named entity linking UMLS Ontology based text mining of gene-phenotype associations: application to candidate gene prediction (232) full articles PubMed. NA ● Applied the WhatIzIt tool, a dictionary-based named entity recognition (NER) tool, to annotate phenotype and gene mentions in full-text articles. ● Used UniProt/Swiss-Prot for annotating gene names and MP/HPO for annotating phenotype names in publications. ● The similarity calculation involved the frequency of classes within sets of entities annotated with classes from the same The Human Phenotype Ontology (234) 202 ontology, using the Best Match Average (BMA) strategy. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications (209) Clinical notes cTAKES ● NER - RE - text classification UMLS - and other controlled vocabulary SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) Ontology-based clinical information extraction from physician’s free-text notes (198) Clinical notes - EHR OB-CIE NER The Patient Clinical Data (PCD) ontology Linking entities through an ontology using word embeddings and syntactic re-ranking (208) free-text (e.g. abstract) NA Text normalization Onto-Biotope ontology DiseaSE: A biomedical text analytics system for disease symptom extraction and characterization(211) Biomedical text documents retrieved from the PubMed database DiseaSE NER (using metamap) UMLS Biological relation extraction and query answering from MEDLINE abstracts using ontology-based text mining (233) MEDLINE abstract BIEQA ontology-based Biological Information Extraction GENIA ontology (235) 203 Automatic concept recognition using the Human Phenotype Ontology reference and test suite corpora (212) PubMed Abstract Bio-LarK NER Human Phenotype Ontology
Thesis/Dissertation
eng
Bioinformatics
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Hsiao, Will
Introduction of omics metadata into biobanking ontology
Text