数据-知识协同驱动的共伴生矿产知识图谱构建方法

doi:10.3799/dqkx.2025.268

数据-知识协同驱动的共伴生矿产知识图谱构建方法

doi: 10.3799/dqkx.2025.268

秦颖¹,
杨慧^1,2, ,,
崔柳¹,
酆格斐³,
王佳⁴,
乔亦娜¹,
吕青宙¹,
冯健⁵,
王文峰^6,1

1. 中国矿业大学资源与地球科学学院, 煤层气资源与成藏过程教育部重点实验室, 徐州 221116;
2. 乌鲁木齐气象卫星地面站, 乌鲁木齐 830002;
3. 江苏师范大学语言科学与艺术学院, 徐州 221116;
4. 格林威治大学计算机与数学科学学院, 伦敦 SE109LS;
5. 新疆维吾尔自治区地质局煤田地质中心, 乌鲁木齐 830000;
6. 新疆大学地质与矿业工程学院, 乌鲁木齐 830047

基金项目:

国家自然科学基金面上项目 (42571545,52478011)

第三次新疆综合科学考察项目(2022xjkk1006)

新疆维吾尔自治区重点研发项目(2022B01012-1)

江苏省自然资源科技计划项目(2023018)

中央高校基本科研业务费专项资金资助(2024ZDPYCH1002)

江苏省科技智库计划项目(JSKX0225042)

详细信息

作者简介:
秦颖（2002-），女，硕士研究生，地球探测与信息技术专业，主要从事矿产资源大数据智能挖掘研究。E-mail：qinying@cumt.edu.cn，ORCID：0009-0008-1683-943X

通讯作者:
杨慧（1983-），女，教授，博士，博士生导师，从事地球信息科学与技术及相关教学工作，主要从事大数据的地球时空信息智能分析研究。E-mail：yanghui@cumt.edu.cn，ORCID：0000-0001-9421-3573

中图分类号: P617;P628;TP18
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出版历程
- 收稿日期: 2025-10-15

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

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 221116, 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

摘要

摘要: 针对地质大数据与成矿知识割裂导致的共伴生关系建模难题，亟需构建支撑智能分析的知识方法体系。本文提出一种数据-知识协同驱动的知识图谱构建方法，融合领域本体与BERT-BiLSTM-CRF模型，通过“知识引导—数据反馈”机制实现本体演化与信息抽取的动态协同，系统地从多源地质文本中提取矿床特征与共伴生关系，建立勘查数据与成矿知识间的语义映射。实验表明：实体识别F1值达83.2%，较基线提升15.4%；实体重复率降低5.7个百分点，图谱一致性显著改善。最终构建包含1.2万节点与2.8万关系的结构化知识图谱，支撑可视化分析、智能问答、成矿预测及平台服务。该方法实现了知识与数据的深度融合，为矿产勘查向数据-知识协同驱动的智能范式转型提供了可解释、可操作的技术路径。
- 知识图谱 /
- 本体 /
- 数据知识协同 /
- 共伴生矿产 /
- 深度学习 /
- Neo4j图数据库
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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参考文献(70)

陈伟,吴友政,陈文亮,等.基于BiLSTM-CRF的关键词自动抽取[J].计算机科学,2018,45(S1):91-96+113.
鄂海红,张文静,肖思琪,等.深度学习实体关系抽取研究综述[J].软件学报,2019,30(06):1793-1818.DOI: 10.13328/j.cnki.jos.005817.
韩伟,张照坤,雷辛亚,等.多源融合的“岩石-矿物-光谱”知识图谱构建及应用[J/OL].地球科学,1-18[2025-11-05].https://link.cnki.net/urlid/42.1874.P.20251010.1336.006.
鞠建华,万会,韩见.我国共伴生矿产资源特点及综合评价要点综述[J].中国矿业,2024,33(12):17-23.
李冬梅,张扬,李东远,等.实体关系抽取方法研究综述[J].计算机研究与发展,2020,57(07):1424-1448.
廖振鑫.面向地质领域的知识图谱构建研究及应用[D].电子科技大学,2021.DOI: 10.27005/d.cnki.gdzku.2021.004734.
邱宇辰.知识图谱可视化平台的设计实现及应用[D].北京邮电大学,2021.DOI: 10.26969/d.cnki.gbydu.2021.003211.
田玲,张谨川,张晋豪,等.知识图谱综述——表示、构建、推理与知识超图理论[J].计算机应用,2021,41(08):2161-2186.
汪陈.面向中文文献的金矿时空属性信息抽取及知识图谱可视化表达[D].合肥工业大学,2021.DOI: 10.27101/d.cnki.ghfgu.2021.000991.
王成彬,王明果,王博,等.融合知识图谱的矿产资源定量预测[J].地学前缘,2024,31(04):26-36.DOI: 10.13745/j.esf.sf.2024.5.3.
王红,张青青,蔡伟伟,等.基于Neo4j的领域本体存储方法研究[J].计算机应用研究,2017,34(08):2404-2407.
王乃钰,叶育鑫,刘露,等.基于深度学习的语言模型研究进展[J].软件学报,2021,32(04):1082-1115.DOI: 10.13328/j.cnki.jos.006169.
王晴,黄进,刘鑫,等.成果地质资料知识图谱构建与可视化[J].计算机系统应用,2022,31(08):140-145.DOI: 10.15888/j.cnki.csa.008637.
吴润泽,李浩,梅红波,等.基于知识图谱检索增强生成的滑坡监测预警系统[J].地球科学,2025,50(10):4125-4136.
吴占伟,黄炳香,赵兴龙,等.我国煤系共伴生矿产赋存特征及分布规律[J].采矿与安全工程学报,2024,41(01):29-46.DOI: 10.13545/j.cnki.jmse.2023.0479.
谢腾,杨俊安,刘辉.基于BERT-BiLSTM-CRF模型的中文实体识别[J].计算机系统应用,2020,29(07):48-55.DOI: 10.15888/j.cnki.csa.007525.
徐浩然,向阳,丁玲.基于大语言模型知识增强和多特征融合的中文命名实体识别方法[J/OL].北京航空航天大学学报,1-12[2025-06-28].https://doi.org/10.13700/j.bh.1001-5965.2024.0421.
杨慧,盛业华,温永宁,等.Web环境下异构地理分析模型的标准化和形式化研究[J].地球信息科学,2008,(03):382-389.
杨军.基于APM的应用性能管理系统的设计与实现[D].南京大学,2020.DOI: 10.27235/d.cnki.gnjiu.2020.002191.
杨秀璋.实体和属性对齐方法的研究与实现[D].北京理工大学,2016.
叶育鑫,刘家文,曾婉馨,等.基于本体指导的矿产预测知识图谱构建研究[J].地学前缘,2024,31(04):16-25.DOI: 10.13745/j.esf.sf.2024.5.4.
张宝一,唐嘉成,张彤蕴,等.大语言模型赋能的地质找矿知识图谱与问答模型构建[J/OL].地球科学,1-24[2025-10-10].https://doi.org/10.3799/dqkx.2025.176.
张前龙,周永章,郭兰萱,等.找矿知识图谱的智能化应用：以钦杭成矿带斑岩铜矿为例[J].地学前缘,2024,31(04):7-15.DOI: 10.13745/j.esf.sf.2024.5.2.
赵鹏大.地质大数据特点及其合理开发利用[J].地学前缘,2019,26(04):1-5.DOI: 10.13745/j.esf.sf.2018.9.8.
赵汀,刘超,李厚民,等.矿产资源国情调查中数据挖掘创新应用与知识图谱构建[J/OL].地学前缘,1-14[2025-07-16].https://doi.org/10.13745/j.esf.sf.2024.11.66.
周成虎,王华,王成善,等.大数据时代的地学知识图谱研究[J].中国科学:地球科学,2021,51(07):1070-1079.
周永章,张前龙,黄永健,等.钦杭成矿带斑岩铜矿知识图谱构建及应用展望[J].地学前缘,2021,28(03):67-75.DOI: 10.13745/j.esf.sf.2021.1.2.
周永章,肖凡.管窥人工智能与大数据地球科学研究新进展[J].地学前缘,2024,31(04):1-6.DOI: 10.13745/j.esf.sf.2024.6.99.
庄子浩,焦守龙,孙琛皓.基于BERT-BiLSTM-CRF模型的地质领域实体识别研究[J].计算机与数字工程,2024,52(06):1815-1820+1876.
左仁广.基于数据科学的矿产资源定量预测的理论与方法探索[J].地学前缘,2021,28(03):49-55.DOI: 10.13745/j.esf.sf.2020.12.1.
Bilenchi, I., Gramegna, F., Loseto, G., et al. Cowl: Pushing OWL 2 over the Edge [J]. Internet of Things, 2025, 29: 101439. DOI: 10.1016/j.iot.2024.101439.
Bull, L., Worden, K., Manson, G., et al. Active Learning for Semi-Supervised Structural Health Monitoring [J]. Journal of Sound and Vibration, 2018, 437: 373–388. DOI: 10.1016/j.jsv.2018.08.040.
Chen, W., Wu, Y.Z., Chen, W.L., et al. Automatic Keyword Extraction Based on BiLSTM-CRF [J]. Computer Science, 2018, 45(S1): 91-96+113.
Du, M.X., Gao, B.Y., Wang, S.Z., et al. Design and Implementation of Time Metrology Vocabulary Ontology [J]. Electronics, 2024, 13(14): 2828. DOI: 10.3390/electronics13142828.
E, H.H., Zhang, W.J., Xiao, S.Q., et al. Survey of Entity Relationship Extraction Based on Deep Learning [J]. Journal of Software, 2019, 30(06): 1793-1818. DOI: 10.13328/j.cnki.jos.005817.
Han, W.; Zhang, Z.K.; Lei, X.Y.; et al. Construction and Application of the “Rock-Mineral-Spectrum” Knowledge Graph Based on Multi-source Fusion. Earth Sci. 2025, 1-18. https://link.cnki.net/urlid/42.1874.P.20251010.1336.006
Ju, J.H., Wan, H., Han, J. Overview of Characteristics and Comprehensive Evaluation points of Associated Mineral Resources in China [J]. China Mining Magazine, 2024, 33(12): 17-23.
Li, D.M., Zhang, Y., Li, D.Y., et al. Review of Entity Relation Extraction Methods [J]. Journal of Computer Research and Development, 2020, 57(07): 1424-1448.
Li, J., Sun, A.X., Han, J.L., et al. A Survey on Deep Learning for Named Entity Recognition [J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(1): 50-70. DOI: 10.1109/TKDE.2020.2981314.
Liao, Z.X. The Construction and Application of Knowledge Graph for Geology [D]. University of Electronic Science and Technology of China, 2021. DOI: 10.27005/d.cnki.gdzku.2021.004734.
Meng, F., Yang, S., Wang, J., et al. Creating Knowledge Graph of Electric Power Equipment Faults Based on BERT–BiLSTM–CRF Model [J]. Journal of Electrical Engineering & Technology, 2022, 17: 2507-2516. DOI: 10.1007/s42835-022-01032-3.
Pérez, J., Díaz, J., Garcia-Martin, J., et al. Systematic literature reviews in software engineering—enhancement of the study selection process using Cohen’s Kappa statistic [J]. Journal of Systems and Software, 2020, 168: 110657. DOI: 10.1016/j.jss.2020.110657.
Qiu, Q., Xie, Z., Wu, L., et al. BiLSTM-CRF for Geological Named Entity Recognition from the Geoscience Literature [J]. Earth Science Informatics, 2019, 12: 565–579. DOI: 10.1007/s12145-019-00390-3.
Qiu, Y.C. Design and Application of Knowledge Graph Visualization Platform [D]. Beijing University of Posts and Telecommunications, 2021. DOI: 10.26969/d.cnki.gbydu.2021.003211.
Tian, L., Zhang, J.C., Zhang, J.H., et al. Knowledge Graph Survey: Representation, Construction, Reasoning and Knowledge Hypergraph Theory [J]. Journal of Computer Applications, 2021, 41(08): 2161-2186.
Wang, C. Extraction of Spatiotemporal Attributes Information of Gold Mines and Visual Expression of Knowledge Graphs for Chinese Literature [D]. Hefei University of Technology, 2021. DOI: 10.27101/d.cnki.ghfgu.2021.000991.
Wang, C.B., Wang, M.G., Wang, B., et al. Knowledge Graph-infused Quantitative Mineral Resource forecasting [J]. Earth Science Frontiers, 2024, 31(04): 26-36. DOI: 10.13745/j.esf.sf.2024.5.3.
Wang, H., Zhang, Q.Q., Cai, W.W., et al. Research on Storage Method of Domain Ontology Based on Neo4j [J]. Application Research of Computers, 2017, 34(08): 2404-2407.
Wang, N.Y., Ye, Y.X., Liu, L., et al. Language Models Based on Deep Learning: A Review [J]. Journal of Software, 2021, 32(04): 1082-1115. DOI: 10.13328/j.cnki.jos.006169.
Wang, Q., Huang, J., Liu, X., et al. Construction and Visualization of Knowledge Graph of Geological Report [J]. Computer Systems & Applications, 2022, 31(08): 140-145. DOI: 10.15888/j.cnki.csa.008637.
Wu, R.Z.; Li, H.; Mei, H.B.; et al. A Landslide Monitoring and Early Warning System with Retrieval-Augmented Generation Enhanced by Knowledge Graph. Earth Sci. 2025, 50(10), 4125-4136.
Wu, Z.W., Huang, B.X., Zhao, X.L., et al. Occurrence Characteristics and Distribution of Paragenetic Commercial Minerals and Associated Minerals in Coal-bearing strata in China [J]. Journal of Mining & Safety Engineering, 2024, 41(01): 29-46. DOI: 10.13545/j.cnki.jmse.2023.0479.
Xie, T., Yang, J.A., Liu, H. Chinese Entity Recognition Based on BERT-BiLSTM-CRF Model [J]. Computer Systems & Applications, 2020, 29(07): 48-55. DOI: 10.15888/j.cnki.csa.007525.
Xu, H.R., Xiang, Y., Ding, L. Chinese Named Entity Recognition Method Based on Knowledge Enhancement from Large Language Models and Multi-feature Fusion [J/OL]. Journal of Beijing University of Aeronautics and Astronautics, 1-12 [2025-06-28]. https://doi.org/10.13700/j.bh.1001-5965.2024.0421.
Yang, H., Sheng, Y.H., Wen, Y.N., et al. Research on Standardization and Formalization of Heterogeneous Geo-analysis Models in Web Space [J]. Geo-Information Science, 2008, (03): 382-389.
Yang, J. Design and Implementation of Application Performance Management System Based on APM [D]. Nanjing University, 2020. DOI: 10.27235/d.cnki.gnjiu.2020.002191.
Yang, X.Z. Research and Implementation of Entity Alignment and Attribute Alignment [D]. Beijing Institute of Technology, 2016.
Ye, Y.X., Liu, J.W., Zeng, W.X., et al. Ontology-guided Knowledge Graph Research Construction for Mineral Predicts [J]. Earth Science Frontiers, 2024, 31(04): 16-25. DOI: 10.13745/j.esf.sf.2024.5.4.
Yin, Q., Zhou, Y., Rao, J.Y. An intelligent prediction method for surface settlement of shield tunnel construction based on CNN-BiLSTM-SA [J]. KSCE Journal of Civil Engineering, 2025, 29(4): 100061. DOI: 10.1016/j.kscej.2024.100061.
Yu, F., Wang, M.G., Wang, C.B., et al. GeoMinLM: A Large Language Model in Geology and Mineral Survey in Yunnan Province [J]. Ore Geology Reviews, 2025, 182: 106638. DOI: 10.1016/j.oregeorev.2025.106638.
Zhang, B.Y., Tang, J.C., Zhang, T.Y., et al. Knowledge Graph and Question-Answering Model for Geological Prospecting Empowered by Large Language Models [J/OL]. Earth Science, 1-24 [2025-10-10]. https://doi.org/10.3799/dqkx.2025.176.
Zhang, L., Lobov, A. Semantic Web Rule Language-based Approach for Implementing Knowledge-Based Engineering Systems [J]. Advanced Engineering Informatics, 2024, 62(Part A): 102587. DOI: 10.1016/j.aei.2024.102587.
Zhang, Q.L., Zhou, Y.Z., Guo, L.X., et al. Intelligent Application of Knowledge Graphs in Mineral Prospecting: A Case Study of Porphyry Copper Deposits in the Qin-Hang Metallogenic Belt [J]. Earth Science Frontiers, 2024, 31(04): 7-15. DOI: 10.13745/j.esf.sf.2024.5.2.
Zhao, P.D. Characteristics and Rational Utilization of Geological Big Data [J]. Earth Science Frontiers, 2019, 26(04): 1-5. DOI: 10.13745/j.esf.sf.2018.9.8.
Zhao, T., Liu, C., Li, H.M., et al. Geological Mineral Data Mining and Knowledge Graph Construction in Mineral Resources National Survey [J/OL]. Earth Science Frontiers, 1-14 [2025-07-16]. https://doi.org/10.13745/j.esf.sf.2024.11.66.
Zhou, C.H., Wang, H., Wang, C.S., et al. Geoscience Knowledge Graph in the Big Data Era [J]. Science China Earth Sciences, 2021, 51(07): 1070-1079.
Zhou, Y.Z., Zhang, Q.L., Huang, Y.J., et al. Constructing Knowledge Graph for the Porphyry Copper Deposits in the Qingzhou-Hangzhou Bay Area：Insight into Knowledge Graph based Mineral Resource Prediction and Evaluation [J]. Earth Science Frontiers, 2021, 28(03): 67-75. DOI: 10.13745/j.esf.sf.2021.1.2.
Zhou, Y.Z., Xiao, F. A Glimpse of the Latest Advances in Artificial Intelligence and Big Data Geoscience Research [J]. Earth Science Frontiers, 2024, 31(04): 1-6. DOI: 10.13745/j.esf.sf.2024.6.99.
Zhuang, Z.H., Jiao, S.L., Sun, C.H. Named Entity Recognition in Geological Field Based on BERT-BiLSTM-CRF [J]. Computer & Digital Engineering, 2024, 52(06): 1815-1820+1876.
Zuo, R.G. Data Science-based Theory and Method for Quantitative Prediction of Mineral Resources [J]. Earth Science Frontiers, 2021, 28(03): 49-55. DOI: 10.13745/j.esf.sf.2020.12.1.

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数据-知识协同驱动的共伴生矿产知识图谱构建方法

doi: 10.3799/dqkx.2025.268

作者简介:
秦颖（2002-），女，硕士研究生，地球探测与信息技术专业，主要从事矿产资源大数据智能挖掘研究。E-mail：qinying@cumt.edu.cn，ORCID：0009-0008-1683-943X

通讯作者:
杨慧（1983-），女，教授，博士，博士生导师，从事地球信息科学与技术及相关教学工作，主要从事大数据的地球时空信息智能分析研究。E-mail：yanghui@cumt.edu.cn，ORCID：0000-0001-9421-3573

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

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数据-知识协同驱动的共伴生矿产知识图谱构建方法

doi: 10.3799/dqkx.2025.268

作者简介: 秦颖（2002-），女，硕士研究生，地球探测与信息技术专业，主要从事矿产资源大数据智能挖掘研究。E-mail：qinying@cumt.edu.cn，ORCID：0009-0008-1683-943X

通讯作者: 杨慧（1983-），女，教授，博士，博士生导师，从事地球信息科学与技术及相关教学工作，主要从事大数据的地球时空信息智能分析研究。E-mail：yanghui@cumt.edu.cn，ORCID：0000-0001-9421-3573

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

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出版历程

目录

作者简介:
秦颖（2002-），女，硕士研究生，地球探测与信息技术专业，主要从事矿产资源大数据智能挖掘研究。E-mail：qinying@cumt.edu.cn，ORCID：0009-0008-1683-943X

通讯作者:
杨慧（1983-），女，教授，博士，博士生导师，从事地球信息科学与技术及相关教学工作，主要从事大数据的地球时空信息智能分析研究。E-mail：yanghui@cumt.edu.cn，ORCID：0000-0001-9421-3573