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.
Li et al. (Wed,) studied this question.