PMinrKG: polymetallic mineral resources knowledge graph construction and its applications integrated with multimodal data

Currently, geological reports and maps of mineral resources contain a wealth of earth science knowledge and expert experience. A key challenge in mineral resource exploration and prediction is the standardization of complex mineral deposit data into structured, analyzable formats, the extraction of relevant knowledge from this data, and its effective application in mineral deposit research. This paper presents an intelligent mining prediction framework based on multimodal data for the construction of a polymetallic mineral resource knowledge graph (PMinrKG). Firstly, using mineral geological survey reports and geological maps as data sources, entity relationship extraction is performed using the current mainstream Universal Information Extraction framework (UIE) and ArcGIS Pro software, and aligned and fused to form PMinrKG. Secondly, we systematically organized the service application of KGs from four dimensions: analysis of the elements of mineralization, semantic understanding based on KG, KG-based intelligent Q&A analysis, and mineral resource relations prediction based on KG embedding. Experimental results indicate that the mineral resources knowledge graph, as a semantic network, can provide valuable insights through in-depth exploration and analysis. By extracting multidimensional information, such as mineral types and associated strata, it offers critical reference value for effectively delineating deep mineral resource exploration areas.