数据采集与处理2025，Vol.40Issue(3)：647-658,12.DOI:10.16337/j.1004-9037.2025.03.007
面向特种设备的大语言模型-知识图谱双向推理优化与幻觉抑制方法
LLM-KG Bidirectional Inference Optimization and Hallucination Suppression for Special Equipment
摘要
Abstract
Existing studies have constructed knowledge graph(KG)intelligent question-answering systems based on large language models(LLMs)in the field of special equipment.However,limited by the inincomplete entity relationships of KG,LLMs are still prone to hallucination in knowledge-intensive tasks.To suppress the generation of hallucinations,the fusion KG reasoning technology is proposed to enhance the knowledge representation by completing the entity relationship links.Furthermore,in view of the deficiencies of the existing KG reasoning methods in semantic association and topological structure parsing,a dynamic reasoning mechanism based on LLM is further introduced.By leveraging its deep semantic understanding ability,high-order logic rules are automatically generated to achieve the precise expansion of KG,thereby constructing a bidirectional collaborative optimization mechanism between LLM and KG.The results show that this method significantly outperforms the baseline model in terms of mean reciprocal rank(MRR),first hit rate(Hits@1),and top ten hit rate(Hits@10)on the Family,Kinship,and UMLS datasets.关键词
知识图谱/大语言模型/幻觉抑制/双向协同优化/特种设备Key words
knowledge graph(KG)/large language model(LLM)/hallucination suppression/bidirectional cooperative optimization/special equipment分类
计算机与自动化引用本文复制引用
郑强,许振彬..面向特种设备的大语言模型-知识图谱双向推理优化与幻觉抑制方法[J].数据采集与处理,2025,40(3):647-658,12.基金项目
福建省科技项目引导性项目(2023H0012). （2023H0012）
