The construction and application of a Knowledge Graph for prospecting mineral resources in the East Tianshan−North Mountain region based on the large language model

The current utilization of the new generation intelligent information-based mineral exploration technology is of great significance for the intelligent information-based mineral exploration. Facing with complex mineralization processes and multi-source heterogeneous geoscientific data, the existing expert experience rules and mineral exploration methods are insufficient to meet the intelligent demands of mineral resource prediction. To address this issue, we have constructed a geological ontology for intelligent mineral exploration in a top-down manner in this paper. Based on this, by focusing on the East Tianshan−North Mountain mineralization belt, using journal articles, conference papers, and mineral resource geological reports related to the East Tianshan−North Mountain region as data sources, we have proposed a method for extracting geological entity relationships based on the combination of the Deepseek-reasoner large language model and the prompt learning, then have extracted 39 types of relationships contained in the data sources and further have constructed a knowledge graph for the mineral exploration in the East Tianshan-North Mountain region. Building on this foundation, in this paper, we have used methods such as the intelligent question-answering and community detection to have validated and applied the knowledge graph for the mineral exploration in the East Tianshan-North Mountain region. These efforts provide robust technological and methodological support for predicting mineral resources within the East Tianshan−North Mountain metallogenic belt.