基于GeoGPT与LightRAG的地质找矿知识图谱及知识问答模型构建方法研究OA

[Objective]The integration of massive geological text data into mineral prospecting is challenged by difficulties in semantic recognition and synthesis.While Large Language Models(LLMs)offer new possibilities,they face significant bottlenecks in understanding domain-specific terminology and constructing knowledge graphs.[Methods]This paper proposes a methodology for constructing a geological prospecting knowledge graph and a question-answering(QA)model by integrating GeoGPT with the LightRAG framework.Leveraging GeoGPT's domain-specific prior knowledge,the approach enables autonomous ontology definition,entity recognition,and relation extraction.A post-processing module for association enhancement and degradation is employed to refine the knowledge graph.Furthermore,a QA model is developed using LightRAG's distinctive dual-layer retrieval and incremental update mechanisms to achieve contextual knowledge completion from external geological databases.[Results]In key entity recognition(e.g.,minerals,ore deposits),GeoGPT's F1-score outperformed general LLMs(DeepSeek-V3,Qwen2.5-72B)by 17%-28%.Compared to GraphRAG,LightRAG significantly improved retrieval efficiency by bypassing high-cost community summarization and global reconstruction.The GeoGPT-based QA model achieved win rates 8%-28%higher in geochemistry and 52%-78%higher in remote sensing geology compared to the general models.[Conclusions]This study provides an efficient method for constructing geological knowledge graphs and lightweight QA models.By substantially improving retrieval efficiency and incremental updating,this methodology offers a robust new paradigm for the intelligent utilization of geological text data.