现代信息科技2026，Vol.10Issue(11)：62-67,6.DOI:10.19850/j.cnki.2096-4706.2026.11.012
基于Graph-RAG的BIM知识图谱智能检索系统研究
Research on Intelligent BIM Knowledge Graph Retrieval System Based on Graph-RAG
摘要
Abstract
To address the issues of inconsistent naming conventions and high retrieval thresholds in Building Information Modeling(BIM)attributes,this paper designs a graph-enhanced retrieval generation system(Graph-RAG)using the DeepSeek-R1 large language model and the Neo4j graph database.The system employs a regular expression-enhanced natural language to Cypher query(NL2Cypher)mapping technique and a logic self-healing interceptor to enable an automated closed-loop workflow from Industry Foundation Classes(IFC)models to engineering audit reports.Experimental results show that the response time during the mapping phase remains below 812 milliseconds.In a test involving 230 components with missing material attributes,the system achieves a 100％instruction success rate through latent semantic mining,completing the task in 61.151 seconds.The study demonstrates that the system effectively overcomes the reliance on standardized modeling data,significantly improves BIM data governance efficiency,and enhances decision-making support,offering a novel approach to the digitalization of engineering audits.关键词
BIM/DeepSeek-R1大模型/知识图谱/逻辑自愈/Graph-RAG/NL2Cypher/工程审计/属性打平Key words
BIM/DeepSeek-R1 large language model/knowledge graph/logic self-healing/Graph-RAG/NL2Cypher/engineering audit/attribute flattening分类
信息技术与安全科学引用本文复制引用
赵津磊..基于Graph-RAG的BIM知识图谱智能检索系统研究[J].现代信息科技,2026,10(11):62-67,6.基金项目
江苏省教育科学规划课题(C/2024/02/04) （C/2024/02/04）
江苏省职教学会2025-2026年度江苏职业教育研究课题(XHYBLX2025284) （XHYBLX2025284）
江苏省高校教育信息化研究课题(2025JSETKT230) （2025JSETKT230）
江苏高校哲学社会科学研究一般项目(2025SJYB1587) （2025SJYB1587）
