Representation learning with explicit and implicit graph structures
department or school: Science, Faculty of
department or school: Computer Science, Department of
data provider: DSpace
degree campus: UBCV
creator: Fatemi, Bahare
issued: 2023-04-24T16:19:47Z
issued: 2023
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: The world around us is composed of objects each having relations with other objects. The objects and relations form a (hyper)graph with objects as nodes and relations between objects as (hyper)edges. When learning, the underlying structure representing the relations between the nodes is either given explicitly in the training set or is implicit and needs to be inferred. This dissertation studies graph representation learning with both explicit and implicit structures. For explicit structure, we first tackle the challenge of enforcing taxonomic information while embedding entities and relations. We prove that some fully expressive models cannot respect subclass and subproperty information. With minimal modifications to an existing knowledge graph completion method, we enable the injection of taxonomic information. A second challenge is in representing explicit structures in relational hypergraphs that contain relations defined on an arbitrary number of entities. While techniques, such as reification, exist that convert non-binary relations into binary ones, we show that current embedding-based methods do not work well out of the box for knowledge graphs obtained through these techniques. We introduce embedding-based methods that work directly with relations of arbitrary arity. We also develop public datasets, benchmarks, and baselines and show experimentally that the proposed models are more effective than the baselines. We further bridge the gap between relational algebra and knowledge hypergraphs by proposing an embedding-based model that can represent relational algebra operations. Having introduced novel architectures for explicitly graph-structured data, we further investigate how models with relational inductive biases can be developed and applied to problems with implicit structures. Graph representation learning models work well when the structure is explicit. However, this structure may not always be available in real-world applications. We propose the Simul…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/84434
note: Representation Learning with Explicitand Implicit Graph StructuresbyBahare FatemiB.Sc., Amirkabir University of Technology, 2015M.Sc., The University of British Columbia, 2017A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinThe Faculty of Graduate and Postdoctoral Studies(Computer Science)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)April 2023© Bahare Fatemi 2023The following individuals certify that they have read, and recommend to theFaculty of Graduate and Postdoctoral Studies for acceptance, the disserta-tion entitled: Representation learning with explicit and implicit graph structuressubmitted by Bahare Fatemi in partial fulfillment of the requirements forthe degree of Doctor of Philosophy in Computer Science.Examining Committee:David Poole, Professor, Computer Science, UBCSupervisorLeonid Sigal, Professor, Computer Science, UBCSupervisory Committee MemberJeff Clune, Associate Professor, Computer Science, UBCUniversity ExaminerDaniel J. McDonald, Associate Professor, Statistics, UBCUniversity ExaminerAdditional Supervisory Committee Members:Frank Wood, Associate Professor, Computer Science, UBCSupervisory Committee MemberWilliam L. Hamilton, Adjunct Professor, Computer Science, McGillSupervisory Committee MemberiiAbstractThe world around us is composed of objects each having relations withother objects. The objects and relations form a (hyper)graph with objectsas nodes and relations between objects as (hyper)edges. When learning, theunderlying structure representing the relations between the nodes is eithergiven explicitly in the training set or is implicit and needs to be inferred.This dissertation studies graph representation learning with both explicitand implicit structures. For explicit structure, we first tackle the challengeof enforcing taxonomic information while embedding entities and relations.We prove that some fully expressive models cannot respect subclass and sub-property information. With minimal…
has type: Thesis/Dissertation
date issued: 2023-05
is shown at: 10.14288/1.0431397
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Poole, David L. (David Lynton), 1958-
type: Text
identifier URI: http://hdl.handle.net/2429/84434