大语言模型驱动的水利智能建造知识图谱自动构建OA

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.