水力发电学报2026，Vol.45Issue(4)：12-26,15.DOI:10.11660/slfdxb.20260402
大语言模型驱动的水利智能建造知识图谱自动构建
Large language model-driven automated construction by knowledge graphs for intelligent construction in hydraulic engineering
摘要
Abstract
Knowledge graphs can efficiently integrate the knowledge of a hydraulic project and advance digitalization significantly.However,traditional methods face challenges in cross-domain ontology construction,high annotation cost,and limited transferability.This study constructs an automated knowledge graph framework that leverages large language models(LLMs)for cross-domain intelligent hydraulic construction.The method has two parts:(1)constructing a shared ontology through terminology discovery,co-occurrence networks,and LLM reasoning to resolve cross-domain semantic inconsistencies;(2)extracting enhanced knowledge,combining prior knowledge,hybrid retrieval,dynamic prompting,and chain-of-thought reasoning to reduce LLM hallucinations.Numerical experiments show the shared ontology achieves structural consistency,with cross-domain knowledge extraction reaching an average F1 score of 84.5,outperforming conventional models.This validates the method's effectiveness in multi-subdomain knowledge integration with reduced annotation requirements.关键词
水利建造/知识图谱/大语言模型/提示工程/文本检索Key words
hydraulic construction/knowledge graph/large language model/prompt engineering/text retrieval分类
建筑与水利引用本文复制引用
王旭东,马刚,张栋梁,瞿同明,周伟..大语言模型驱动的水利智能建造知识图谱自动构建[J].水力发电学报,2026,45(4):12-26,15.基金项目
国家自然科学基金项目(52322907 （）
52579134 （）
U23B20149) （）
