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<p>In recent years, geologists have gathered substantial volumes of geological data on mineral resources through extensive field exploration, encompassing a rich body of geological knowledge. A mineral resources knowledge graph (MRKG) can reveal interrelationships between different mineral resources and their characteristics by structuring geological data and knowledge related to mineral resources. However, how to mine information from an existing MRKG to provide services is a challenge that needs to be solved urgently. To address this issue, this paper utilizes a constructed MRKG as the data source and applies data mining and natural language processing techniques to mine and analyze the knowledge graph from two perspectives: common features and individual features. The analysis primarily focuses on five key aspects: mineralization characteristics, mineral symbiosis and associations, rock distribution patterns, mineral element distribution, and spatiotemporal variation laws. Additionally, the application scenarios of the MRKG are systematically explored. Experimental results demonstrate that data mining of the MRKG can effectively uncover the underlying patterns and relationships within mineral resources. This paper not only systematically elaborates on the application scenarios of mineral resources but also provides accurate prediction and decision support for the development and management of mineral resources, helping to discover potential value and opportunities, and providing a new approach for intelligent mineral exploration and mineral prospectivity mapping.</p>

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Advancing Mineral Resource Knowledge Graph Applications Through Data Mining Approaches

  • 期刊论文

  • 发表日期：

    2026年3月16日

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  • Miao Tian,
  • Zhong Xie,
  • Qirui Wu,
  • Qinjun Qiu,
  • Jianguo Chen,
  • Liufeng Tao

摘要

In recent years, geologists have gathered substantial volumes of geological data on mineral resources through extensive field exploration, encompassing a rich body of geological knowledge. A mineral resources knowledge graph (MRKG) can reveal interrelationships between different mineral resources and their characteristics by structuring geological data and knowledge related to mineral resources. However, how to mine information from an existing MRKG to provide services is a challenge that needs to be solved urgently. To address this issue, this paper utilizes a constructed MRKG as the data source and applies data mining and natural language processing techniques to mine and analyze the knowledge graph from two perspectives: common features and individual features. The analysis primarily focuses on five key aspects: mineralization characteristics, mineral symbiosis and associations, rock distribution patterns, mineral element distribution, and spatiotemporal variation laws. Additionally, the application scenarios of the MRKG are systematically explored. Experimental results demonstrate that data mining of the MRKG can effectively uncover the underlying patterns and relationships within mineral resources. This paper not only systematically elaborates on the application scenarios of mineral resources but also provides accurate prediction and decision support for the development and management of mineral resources, helping to discover potential value and opportunities, and providing a new approach for intelligent mineral exploration and mineral prospectivity mapping.