Knowledge Graph and Question-Answering Model for Geological Prospecting Empowered by Large Language Models

Current applications of Large Language Models (LLMs) in geological prospecting face challenges including insufficient domain expertise, data privacy concerns, and model hallucinations. Furthermore, there remains a lack of efficient and rapid knowledge recommendation methods for LLMs in this field. This study proposes a KG-RAG (Knowledge Graph-Embedded Retrieval-Augmented Generation) framework that automates the extraction and structured representation of geological prospecting knowledge under the constraints of a geological ontology, leveraging large LLMs as tools. It further employs multi-hop retrieval algorithms within the knowledge graph to enhance the depth and breadth of retrieved content, thereby constructing an intelligent question-answering model for geological prospecting. Experimental results demonstrate that KG-RAG achieves scores of 0.807 (Precision), 0.833 (Recall), and 0.819 (F1-score) in knowledge graph construction tasks. Compared to direct knowledge extraction using the baseline LLM (GLM4-9B), KG-RAG delivers improvements of approximately 50% (Precision), 8% (Recall), and 29% (F1-score), respectively. In question-answering tasks, KG-RAG achieves 0.917 (Recall) and 0.88 (Precision), outperforming document vector-embedded retrieval-augmented generation methods by approximately 24% (Recall) and 22% (Precision), respectively. KG-RAG exhibits superior performance in both knowledge graph construction and intelligent question-answering. It effectively collects and represents geological prospecting and mineral exploration knowledge, providing a valuable reference to geologists for the combined application of LLMs and knowledge graphs.