Construction and applications of knowledge graph of porphyry copper deposits

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A knowledge graph is becoming popular due to its ability to describe the real world by using a graph language that can be understood by both humans and machines using computer technologies. A case study to construct the knowledge graph of porphyry copper deposits is presented in this paper. First of all, the raw text data is collected and integrated from selected porphyry copper deposits and porphyry-skarn copper deposits in the Qinzhou Bay – Hangzhou Bay metallogenic belt, South China. Second, the text's entities, relations, and attributes are labeled and extracted with reference to the conceptual model of porphyry copper deposits in the study area. The third, a knowledge graph of porphyry copper deposits, was constructed using Neo4j 4.3. The resulted knowledge graph of porphyry copper deposit has the basic functions of an application. Furthermore, as part of a planned integrated knowledge graph from a single deposit, through an upper-geared metallogenic series, to a high-top metallogenic province, the understanding from the present study may be extended to mineral resource prospectivity and assessment beyond today. The interrelationship between the earth system, the metallogenic system, the exploration system, and the prospectivity and assessment (ES-MS-ES-PS) should be completely understood, and a knowledge graph system for ES-MS-ES-PS is needed. The key scientific and technological problems for achieving the ES-MS-ES-PS knowledge graph system are included in the progressively relative system of the domain ontology and knowledge graph of ES-MS-ES-PS, the automatic construction technology of complicated ESMS-ES-PS domain ontology and knowledge graph, the self-evolution and complementary techniques for multi-modal correlation data embedding in the ES-MS-ES-PS knowledge graph, and the knowledge graph, big data mining and artificial intelligence based on ES-resource prospectivity, and assessment theory, and methods.

About the authors

Yongzhang Zhou

Sun Yat-sen University; Guangdong Provincial Key Lab of Geological Processes and Mineral Resource Survey

Email: zhouyz@mail.sysu.edu.cn
ORCID iD: 0000-0002-8572-5849

Qianlong Zhang

Sun Yat-sen University; Guangdong Provincial Key Lab of Geological Processes and Mineral Resource Survey

Email: zhouyz@mail.sysu.edu.cn
ORCID iD: 0000-0002-8572-5849

Wenjie Shen

Sun Yat-sen University; Guangdong Provincial Key Lab of Geological Processes and Mineral Resource Survey

Email: zhouyz@mail.sysu.edu.cn
ORCID iD: 0000-0002-8572-5849

Fan Xiao

Sun Yat-sen University; Guangdong Provincial Key Lab of Geological Processes and Mineral Resource Survey

Yanlong Zhang

Guangdong Provincial Key Lab of Geological Processes and Mineral Resource Survey

Shiwu Zhou

Guangdong Provincial Key Lab of Geological Processes and Mineral Resource Survey

Yongjian Huang

Guangdong Xuanyuan Network Tech. Inc.

Junjie Ji

Sun Yat-sen University; Guangdong Provincial Key Lab of Geological Processes and Mineral Resource Survey

Lei Tang

Sun Yat-sen University; Guangdong Provincial Key Lab of Geological Processes and Mineral Resource Survey

Chong Ouyang

Sun Yat-sen University; Guangdong Provincial Key Lab of Geological Processes and Mineral Resource Survey

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