LLM-Enhanced Framework for Building Domain-Specific Lexicon for Urban Power Grid Design
department or school: Applied Science, Faculty of
department or school: Non UBC
department or school: Electrical and Computer Engineering, Department of
data provider: DSpace
identifier citation: Applied Sciences 15 (8): 4134 (2025)
creator: Xu, Yan
creator: Wang, Tao
creator: Yuan, Yang
creator: Huang, Ziyue
creator: Chen, Xi
creator: Zhang, Bo
creator: Zhang, Xiaorong
creator: Wang, Zehua
issued: 2025-05-14T21:07:33Z
issued: 2025-04-09
description: Traditional methods for urban power grid design have struggled to meet the demands of multi-energy integration and high resilience scenarios due to issues such as delayed updates of terminology and semantic ambiguity. Current techniques for constructing domain-specific lexicons face challenges like the insufficient coverage of specialized vocabulary and imprecise synonym mining, which restrict the semantic parsing capabilities of intelligent design systems. To address these challenges, this study proposes a framework for constructing a domain-specific lexicon for urban power grid design based on Large Language Models (LLMs). The aim is to enhance the accuracy and practicality of the lexicon through multi-level term extraction and synonym expansion. Initially, a structured corpus covering national and industry standards in the field of power was constructed. An improved Term Frequency–Inverse Document Frequency (TF-IDF) algorithm, combined with mutual information and adjacency entropy filtering mechanisms, was utilized to extract high-quality seed vocabulary from 3426 candidate terms. Leveraging LLMs, multi-level prompt templates were designed to guide synonym mining, incorporating a self-correction mechanism for semantic verification to mitigate errors caused by model hallucinations. This approach successfully built a domain-specific lexicon comprising 3426 core seed words and 10,745 synonyms. The average cosine similarity of synonym pairs reached 0.86, and expert validation confirmed an accuracy rate of 89.3%; text classification experiments showed that integrating the domain-specific dictionary improved the classifier’s F1-score by 9.2%, demonstrating the effectiveness of the method. This research innovatively constructs a high-precision terminology dictionary in the field of power design for the first time through embedding domain-driven constraints and validation workflows, solving the problems of insufficient coverage and imprecise expansion of traditional meth…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/91141
note: Academic Editor: Len GelmanReceived: 27 February 2025Revised: 3 April 2025Accepted: 6 April 2025Published: 9 April 2025Citation: Xu, Y.; Wang, T.; Yuan, Y.;Huang, Z.; Chen, X.; Zhang, B.; Zhang,X.; Wang, Z. LLM‑EnhancedFramework for Building Domain‑Specific Lexicon for Urban PowerGrid Design. Appl. Sci. 2025, 15, 4134.https://doi.org/10.3390/app15084134Copyright: © 2025 by the authors.LicenseeMDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license(https://creativecommons.org/licenses/by/4.0/).ArticleLLM‑Enhanced Framework for Building Domain‑SpecificLexicon for Urban Power Grid DesignYan Xu 1, TaoWang 1, Yang Yuan 1, Ziyue Huang 1, Xi Chen 2,* , Bo Zhang 3 , Xiaorong Zhang 2 and ZehuaWang 41 State Grid Jiangsu Electric Power Design Consulting Co., Ltd., Xuzhou Survey and Design BranchCompany, Xuzhou 221000, China; 17805499284@163.com (Y.X.); 18606172522@163.com (T.W.);19851625313@aliyun.com (Y.Y.); 17358626339@aliyun.com (Z.H.)2 School of Mechanical and Civil Engineering, China University of Mining and Technology,Xuzhou 221000, China; ts22030290a31tm@cumt.edu.cn3 School of Computer Science & Technology, China University of Mining and Technology,Xuzhou 221000, China; zbcumt@163.com4 Department of Electrical and Computer Engineering, The University of British Columbia,Vancouver, BC V6T 1Z4, Canada; zwang@ece.ubc.ca* Correspondence: x.chen@cumt.edu.cnAbstract: Traditional methods for urban power grid design have struggled to meet thedemands of multi‑energy integration and high resilience scenarios due to issues such asdelayedupdates of terminology and semantic ambiguity. Current techniques for construct‑ing domain‑specific lexicons face challenges like the insufficient coverage of specializedvocabulary and imprecise synonym mining, which restrict the semantic parsing capabil‑ities of intelligent design systems. To address these challenges, this study proposes aframework …
has type: Article
is shown at: 10.14288/1.0448883
language: eng
peer review status: Reviewed
provider: Vancouver : University of British Columbia Library
publisher: Multidisciplinary Digital Publishing Institute
publisher DOI: 10.3390/app15084134
rights: CC BY 4.0
rights URI: https://creativecommons.org/licenses/by/4.0/
scholar level: Faculty
scholar level: Researcher
subject: electric power grid design
subject: domain-specific lexicon
subject: synonym mining
subject: large language models
subject: natural language processing
type: Text
identifier URI: http://hdl.handle.net/2429/91141