This analysis demonstrates how Geographic Information System techniques enhance mineral prospecting knowledge graph development, improving predictive outcomes in Xinjiang.
Geographic Information System (GIS) layers contain both spatial precision and domain knowledge, making them valuable for mineral prospectivity analysis. This study proposes a task-oriented methodology to construct a mineral prospecting knowledge graph directly from GIS maps. The framework integrates ontology construction, spatiotemporal semantic embedding, and triple confidence evaluation. Ontologies are built from GIS layers through terminology extraction and alignment with existing standards, while spatial and temporal semantics are encoded using GeoSPARQL and the Geological Time Ontology. Graph Convolutional Networks (GCN) combined with the TransE embedding model are then applied to assess triple plausibility. A case study in the Eastern Tianshan region of Xinjiang verifies the effectiveness of the proposed method through semantic evaluation and graph-theoretic analysis. Guided by GIS, ontology construction significantly enhances the semantic fidelity and structural robustness of the prospecting knowledge graph, providing relatively reliable support for subsequent reasoning and predictive studies.
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Liu et al. (2025) studied this question.
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