Construction and Application of the "Concept-Function" Dual-Axis Ontology in the Knowledge Graph of Mineral Deposits

With the advancement of large language models and knowledge graph technologies, the construction of geoscience knowledge systems based on semantic modeling and logical reasoning has gradually become an important technical approach for intelligent mineral exploration. To address the long-standing issue of single-level ontological semantics in geoscience knowledge representation, this paper proposes a dual-axis ontology design centered on the "concept axis-function axis." This design integrates key geological elements such as stratigraphy, lithology, structure, and magmatic rocks, combined with exploration indicators including ore-bearing lithology, ore-controlling structures, and geochemistry, to construct a concept-function dual-axis ontology structure. Leveraging large language models, entity recognition and relation extraction from geological texts are achieved, and the Neo4j platform is utilized for graph-structured storage and reasoning applications of mineral deposit knowledge. Taking the Qukulekedong Gold-Antimony Ore Concentration Area as a case study, the dual-axis ontology is embedded into prompt engineering to enable knowledge extraction in the study area using large language models. Through semantic association and structural analysis of the knowledge graph, logical reasoning and pattern induction for exploration models are conducted. The results indicate that the dual-axis ontology model, by collaboratively representing geoscience entities through the dual semantic dimensions of the concept axis and function axis, demonstrates significant advantages in semantic retrieval, relationship organization, and knowledge reasoning. This model overcomes the limitations of traditional geological knowledge, which primarily relies on static attribute descriptions, and expands from structural representation to functional semantics. It provides a solid theoretical foundation and methodological support for constructing geoscience knowledge systems with intelligent representation and logical reasoning capabilities.