信息工程大学学报2026，Vol.27Issue(4)：450-456,465,8.DOI:10.3969/j.issn.1671-0673.2026.04.010
面向动态知识检索的大语言模型与知识图谱协同推理方法
Collaborative Reasoning Method Between LLMs and Knowledge Graphs via Dynamic Knowledge Retrieval
摘要
Abstract
To address the issues of static knowledge and factual hallucinations in large language mod-els(LLMs),as well as the incomplete coverage and delayed updates in knowledge graphs,a collabora-tive reasoning framework integrating LLMs and knowledge graphs is proposed via dynamic knowledge retrieval.A closed-loop mechanism is designed to implement dynamic knowledge retrieval,collabora-tive reasoning,and incremental updating.Firstly,a subgraph-enhanced retrieval strategy is adopted,which extracts relevant subgraphs from the knowledge graph through question decomposition and multi-hop reasoning to improve the recall under complex queries.Secondly,semantically relevant pas-sages are retrieved from unstructured texts through a multi-dimensional hybrid weighting method to supplement missing knowledge.Finally,LLMs are utilized to integrate multi-source knowledge for rea-soning,generating answers while extracting new triples for dynamically updating the knowledge graph.Experiments demonstrate that the proposed method can effectively handle complex multi-hop ques-tions,reduce factual hallucinations,and ensure timeliness and continuous evolution of the knowledge base through closed-loop updating.关键词
知识推理/大语言模型/知识图谱/检索增强生成Key words
knowledge reasoning/LLMs/knowledge graph/retrieval-augmented generation分类
信息技术与安全科学引用本文复制引用
夏毅,贺浩峰,陈静,周刚,郑嘉丽,张凯翔..面向动态知识检索的大语言模型与知识图谱协同推理方法[J].信息工程大学学报,2026,27(4):450-456,465,8.基金项目
国家自然科学基金(42371438) （42371438）
河南省科技攻关项目(222102210081,222300420590) （222102210081,222300420590）
