Abstract
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.
Highlights
In the present era of big data, data grows explosively, and it tends to be massive, heterogeneous, and loosely organized, bringing serious challenges to effective access to information and knowledge
In the era of big data and artificial intelligence, there is an urgent need for a language that people and machines can understand together to extend the human brain
(2) The construction of the knowledge graph of porphyry copper deposits is a good experiment, it may be well extended to the epithermal metallogenic system and the Qinzhou Bay – Hangzhou Bay, metallogenic belt, South China, resulting in a complete knowledge graph system from the single deposit, through metallogenic series, to an important metallogenic area
Summary
In the present era of big data, data grows explosively, and it tends to be massive, heterogeneous, and loosely organized, bringing serious challenges to effective access to information and knowledge. It is opment tool of ontology construction in the senecessary for the real scene to iterated or evolve mantic web It provides the construction of ontoland improve the full knowledge graph according ogy concept class, relationship, attribute and into the application feedback, the emerging stance, and shields the specific ontology descripknowledge of the same type, and the new tion language. (2) The initial data acquisition, and the entity, relationship, and attribute annotation and extraction based on the conceptual model of porphyry copper deposit. Based on the existing geological survey reports and other unstructured and semi-structured data, through ontology construction, knowledge extraction, knowledge disambiguation, and knowledge fusion, the knowledge graph of porphyry copper deposit may well be constructed.the knowledge graph of epithermal metallogenic system (Fig. 4) and Qinzhou Bay – Hangzhou Bay metallogenic belt (Fig. 5) can be constructed.
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