Research on the Construction and Application of a Water Conservancy Facility Safety Knowledge Graph Based on Large Language Models

Water conservancy safety management faces several challenges. These include the integration of multi-source heterogeneous data and inefficient knowledge utilization. To address these issues, this study proposes a knowledge graph (KG) construction method that combines ontology modeling with large language models (LLMs). First, an ontology for water conservancy facility safety is constructed, encompassing four core elements: agencies and personnel, engineering equipment, risks and hidden dangers, and systems and processes. Subsequently, a KG-LLM-GraphRAG architecture is designed, which optimizes the knowledge extraction effectiveness of LLM through ontology-constrained prompt templates and utilizes the Neo4j graph database for knowledge storage and multi-hop reasoning. Experimental results demonstrate that the proposed method significantly outperforms traditional approaches in entity-relationship extraction tasks. The resulting KG supports hazard identification, emergency decision-making, and knowledge reuse, offering an efficient tool for organizing and reasoning in water conservancy safety management, strongly propelling the digital transformation of the water conservancy industry.