数据-知识协同驱动的共伴生矿产知识图谱构建方法

秦颖; 杨慧; 崔柳; 酆格斐; 王佳; 乔亦娜; 吕青宙; 冯健; 王文峰

doi:10.3799/dqkx.2025.268

Volume 51 Issue 2

Feb. 2026

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Article Navigation > Earth Science > 2026 > 51(2): 674-689

Qin Ying, Yang Hui, Cui Liu, Feng Gefei, Wang Jia, Qiao Yina, Lv Qingzhou, Feng Jian, Wang Wenfeng, 2026. Developing a Data-Knowledge Synergy-Driven Methodology for Co-Associated Minerals Knowledge Graph Construction. Earth Science, 51(2): 674-689. doi: 10.3799/dqkx.2025.268

Citation:

Qin Ying, Yang Hui, Cui Liu, Feng Gefei, Wang Jia, Qiao Yina, Lv Qingzhou, Feng Jian, Wang Wenfeng, 2026. Developing a Data-Knowledge Synergy-Driven Methodology for Co-Associated Minerals Knowledge Graph Construction. Earth Science, 51(2): 674-689. doi: 10.3799/dqkx.2025.268

Citation:

Qin Ying, Yang Hui, Cui Liu, Feng Gefei, Wang Jia, Qiao Yina, Lv Qingzhou, Feng Jian, Wang Wenfeng, 2026. Developing a Data-Knowledge Synergy-Driven Methodology for Co-Associated Minerals Knowledge Graph Construction. Earth Science, 51(2): 674-689. doi: 10.3799/dqkx.2025.268

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Developing a Data-Knowledge Synergy-Driven Methodology for Co-Associated Minerals Knowledge Graph Construction

doi: 10.3799/dqkx.2025.268

Qin Ying^{1
,
,},
Yang Hui^{1, 2
,
,
,},
Cui Liu¹,
Feng Gefei³,
Wang Jia⁴,
Qiao Yina¹,
Lv Qingzhou¹,
Feng Jian⁵,
Wang Wenfeng^{1, 6}

1.
Key Laboratory of Coalbed Methane Resources & Reservoir Formation Process Ministry of Education, School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China
2.
Urumqi Meteorological Satellite Ground Station, Urumqi 830002, China
3.
School of Linguistic Sciences and Arts, Jiangsu Normal University, Xuzhou 221009, China
4.
School of Computing and Mathematical Sciences, University of Greenwich, London SE109LS, United Kingdom
5.
Coalfield Geological Center, Geological Bureau of Xinjiang Uygur Autonomous Region, Urumqi 830000, China
6.
School of Geology and Mining Engineering, Xinjiang University, Urumqi 830047, China

Received Date: 2025-04-11
Publish Date: 2026-02-25

Abstract

Abstract

The growing disconnect between geological big data and metallogenic knowledge poses a significant challenge to modeling co-associated mineral relationships, underscoring the urgent need for a knowledge-based methodology capable of supporting intelligent analysis. To address this, we propose a data-knowledge synergy-driven approach for constructing knowledge graphs, which integrates domain ontology with the BERT-BiLSTM-CRF model. By leveraging a "knowledge-guided, data-informed" mechanism, the method enables dynamic collaboration between ontology evolution and information extraction, systematically identifying ore deposit features and co-associated relationships from multi-source geological texts and establishing semantic mappings between exploration data and metallogenic knowledge. Experimental results show that entity recognition achieves an F1 score of 83.2%, representing a 15.4 percentage-point improvement over the baseline; entity redundancy is reduced by 5.7 percentage points, markedly enhancing graph consistency. The resulting structured knowledge graph, which comprises 12, 000 nodes and 28, 000 relations, has been deployed in visualization analysis, intelligent question answering, mineralization prediction, and data platform services. This work realizes deep integration of data and knowledge, offering an interpretable and actionable technical pathway for transforming mineral exploration from an experience-driven paradigm to one driven by data-knowledge synergy.
- knowledge graph,
- ontology,
- data-knowledge synergy,
- symbiotic and associated minerals,
- deep learning,
- Neo4j graph database

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