Construction Method of Campus Safety Knowledge Graph Based on Retrieval-Augmented

LLMs suffer from the hallucination problem in knowledge extraction within the campus safety domain, which impairs the accuracy of extracting campus safety entities and relationships. To address this issue, this paper proposes a retrieval-augmented method for constructing a campus safety knowledge graph. The proposed methodology incorporates rule-based text segmentation of campus safety texts, pre-entity extraction from campus safety data, and similarity checks on each pre-entity to achieve entity disambiguation. Next, it utilizes a general LLM for inter-entity relationship extraction to build an initial campus safety knowledge graph. Finally, the method performs vector similarity retrieval on the campus safety data and the initial knowledge graph to obtain relevant text chunks and subgraphs, which are then input into the LLM via prompts to construct a more comprehensive campus safety knowledge graph. On the MINE dataset, the proposed method achieved 71.55% entity faithfulness; on the campus safety dataset, it achieved 79.85% entity faithfulness. These results are 8.62% and 8.30% higher than those of KGGen and GraphRAG, respectively, demonstrating that the proposed method enhances the accuracy of extracting campus safety entities and relationships from LLMs.