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

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^{1, 6}

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, 江苏徐州 221116
2.
乌鲁木齐气象卫星地面站, 新疆乌鲁木齐 830002
3.
江苏师范大学语言科学与艺术学院, 江苏徐州 221009
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, 新疆乌鲁木齐 830000
6.
新疆大学地质与矿业工程学院, 新疆乌鲁木齐 830047

基金项目:

国家自然科学基金面上项目 42571545

国家自然科学基金面上项目 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. ORCID：0009-0008-1683-943X. E-mail：qinying@cumt.edu.cn

通讯作者:
杨慧，ORCID：0000-0001-9421-3573. E-mail：yanghui@cumt.edu.cn

中图分类号: P617; P628; TP18
计量
- 文章访问数: 176
- HTML全文浏览量: 32
- PDF下载量: 23
- 被引次数: 0
出版历程
- 收稿日期: 2025-04-11
- 刊出日期: 2026-02-25

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

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

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. 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
注释:

1) 附件见：https://doi.org/10.3799/dqkx.2025.268.

HTML全文

图 1 原始语料文本类型分布

Fig. 1. Distribution of text types in the original corpus

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图 2 知识引导与数据反馈的协同建模流程

Fig. 2. Flowchart of the knowledge-driven and data-enhanced collaborative modeling processes

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图 3 共伴生矿产知识图谱技术路线图

Fig. 3. Technical roadmap for the construction of co-associated mineral resources knowledge graph

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图 4 BERT-BiLSTM-CRF混合抽取模型

Fig. 4. BERT-BiLSTM-CRF hybrid extraction model

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图 5 基于BERT的语义关系抽取模型

Fig. 5. BERT-Based semantic relation extraction model

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图 6 共伴生矿产知识图谱可视化与特征分析

由于印刷限制，详细a，b图见电子附件

Fig. 6. Visualization and feature analysis of the knowledge graph for Co-associated Minerals

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图 7 智能问答系统架构与功能示例

Fig. 7. Architecture and functional examples of the intelligent question-answering system

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图 8 知识图谱驱动的成矿预测示例

Fig. 8. Example of mineral prospectivity prediction using the co-associated mineral knowledge graph

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图 9 共伴生矿产大数据平台界面（示例）

Fig. 9. Interface of the co-associated mineral data platform

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表 1 矿产领域数据类型

Table 1. Data types in mineral resource domain

数据类型	典型形式	语义特征
结构化数据	统计表单、关系型数据库等	预定义字段与关系，严格模式约束
半结构化数据	XML/JSON报告、标签化地质图件等	嵌套层级，部分字段标记
非结构化数据	地质调查报告、科研论文、勘查日志等	自由文本，隐含领域知识

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表 2 共伴生矿产本体五元组结构

Table 2. Five-tuple structure of the co-associated minerals ontology

所属本体	成分	符号表示	含义说明
CM_Ontology	概念（Concept）	CM_Concept	领域核心术语集合，如矿区、矿床、矿段、矿体等
	属性（Property）	CM_Property	实体特征信息，如成矿年代、构造位置、矿体厚度等
	关系（Relation）	CM_Relation	实体间语义关联，如共生、包含、继承、实例等
	规则（Rule）	CM_Rule	约束矿种组合与成因联系的逻辑规则
	实例（Instance）	CM_Instance	概念到具体对象的映射，如“金堆城钼矿”对应“斑岩型钼矿”类型

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表 3 共伴生矿产实体多维语义特征分类

Table 3. Multi-dimensional semantic feature classification of co-associated mineral entities

维度	子类目	描述说明
时间	成矿时代	指矿产形成所处的具体地质历史阶段，通常以地质纪或世等单位表示，如侏罗纪晚期、早白垩世等
时间	发现时间	矿产地首次在文献中被记录的时间
大地构造位置	地名	矿产所在的具体地理区域或矿区名称，用于标识其空间归属
大地构造位置	经纬度	标注矿产实体中心位置的地理经纬度信息，用于精确定位
类型		根据成因、矿物组合及构造背景对矿床进行分类
成矿地质过程	成矿地质作用	矿床形成的地质作用机制，包括内生、外生和变质成矿作用等
成矿地质过程	成矿构造特征	控制矿体形成、定位和分布的主要构造要素，如断裂带、褶皱构造等
空间	走向	描述矿体在空间上的延伸方向
空间	形态	描述矿体在空间上的展布形态，如似层状、脉状、透镜状等
产状	倾向	矿产实体倾斜面的水平投影方向，用于表征其空间产出方位
产状	倾角	矿产实体倾斜面与水平面之间的夹角，反映其倾斜程度
规模	规模等级	对矿体资源量和体量的定性划分，分为大型、中型、小型等
	长度	矿体沿某一方向延伸的长度，用于定量描述其几何规模
	宽度	矿体横向扩展的宽度，是衡量矿体规模的重要参数之一
	厚度	矿体在垂直或倾斜方向上的厚度，影响矿产开采价值和经济性
共伴生矿种	矿物名称	矿产实体中主要共存或伴生的金属或非金属元素种类
	含量	元素在矿石中的平均含量，常以百分比（%）或克/吨（g/t）表示
	共伴生元素赋存状态	元素在矿物中的存在形式，如独立矿物、类质同象、吸附态、包裹体等形式

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表 4 实体与属性信息抽取结果

Table 4. Extraction results of entities and attributes information

模型	任务指标
	实体识别			属性识别
	precision	recall	F1-score	precision	recall	F1-score
BiLSTM-CRF	67.9%	67.7%	67.8%	60.6%	57.9%	59.2%
BERT-CRF	76.9%	73.3%	75.1%	68.1%	68.8%	68.4%
BERT-BiLSTM-CRF	79.9%	86.7%	83.2%	80.1%	80.6%	80.3%

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表 5 关系抽取结果示例

Table 5. Relation extraction results

原始文本	实体1	实体2	预测关系	真实标签	是否正确
金堆城钼矿床主要共伴生矿种有黄铁矿、黄铜矿	黄铁矿	黄铜矿	共生	共生	是
个旧锡矿位于云南东南有色金属成矿带西端，矿床中锡、铜等元素呈现共伴生形态	锡	铜	共生	共生	是
当前已经完成了矿区的详查工作，在钽铌铷铍矿中发现矿石中还含有大量的共（伴）生矿产，如铍和铷	铍	铷	伴生	伴生	是

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表 6 知识融合前后关键指标对比

Table 6. Comparison of key metrics before and after knowledge fusion

指标	知识融合前	知识融合后	变化幅度
实体重复率（重复实体数量占比）	11.40%	5.70%	↓5.7%
实体表述标准化率	67.20%	83.50%	↑16.3%
知识图谱节点数量（实体总数）	12 553	10 800	↓14.0%
知识图谱边数量（关系总数）	34 200	32 100	↓6.1%
人工校验工作量（需审核实体对数量）	2 522	1 104	↓56.2%

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参考文献(68)

Bilenchi, I., Gramegna, F., Loseto, G., et al., 2025. Cowl: Pushing OWL 2 over the Edge. Internet of Things, 29: 101439. https://doi.org/10.1016/j.iot.2024.101439
Bull, L., Worden, K., Manson, G., et al., 2018. Active Learning for Semi-Supervised Structural Health Monitoring. Journal of Sound and Vibration, 437: 373-388. https://doi.org/10.1016/j.jsv.2018.08.040
Chen, W., Wu, Y. Z., Chen, W. L., et al., 2018. Automatic Keyword Extraction Based on BiLSTM-CRF. Computer Science, 45(S1): 91-96, 113(in Chinese with English abstract).
Du, M. X., Gao, B. Y., Wang, S. Z., et al., 2024. Design and Implementation of Time Metrology Vocabulary Ontology. Electronics, 13(14): 2828. https://doi.org/10.3390/electronics13142828
E, H. H., Zhang, W. J., Xiao, S. Q., et al., 2019. Survey of Entity Relationship Extraction Based on Deep Learning. Journal of Software, 30(6): 1793-1818(in Chinese with English abstract).
Han, W., Zhang, Z. K., Lei, X. Y., et al., 2025. Construction and Application of the "Rock-Mineral-Spectrum" Knowledge Graph Based on Multi-Source Fusion. Earth Science, 1-18[2025-11-05]. https://link.cnki.net/urlid/42.1874.P.20251010.1336.006(in Chinese with English abstract).
Ju, J. H., Wan, H., Han, J., 2024. Overview of Characteristics and Comprehensive Evaluation Points of Associated Mineral Resources in China. China Mining Magazine, 33(12): 17-23(in Chinese with English abstract).
Li, D. M., Zhang, Y., Li, D. Y., et al., 2020. Review of Entity Relation Extraction Methods. Journal of Computer Research and Development, 57(7): 1424-1448(in Chinese with English abstract).
Li, J., Sun, A. X., Han, J. L., et al., 2022. A Survey on Deep Learning for Named Entity Recognition. IEEE Transactions on Knowledge and Data Engineering, 34(1): 50-70. https://doi.org/10.1109/TKDE.2020.2981314
Liao, Z. X., 2021. The Construction and Application of Knowledge Graph for Geology(Dissertation). University of Electronic Science and Technology of China, Chengdu(in Chinese with English abstract).
Meng, F. Q., Yang, S. S., Wang, J. D., et al., 2022. Creating Knowledge Graph of Electric Power Equipment Faults Based on BERT-BiLSTM-CRF Model. Journal of Electrical Engineering & Technology, 17(4): 2507-2516. https://doi.org/10.1007/s42835-022-01032-3
Pérez, J., Díaz, J., Garcia-Martin, J., et al., 2020. Systematic Literature Reviews in Software Engineering: Enhancement of the Study Selection Process Using Cohen's Kappa Statistic. Journal of Systems and Software, 168: 110657. https://doi.org/10.1016/j.jss.2020.110657
Qiu, Q. J., Xie, Z., Wu, L., et al., 2019. BiLSTM-CRF for Geological Named Entity Recognition from the Geoscience Literature. Earth Science Informatics, 12(4): 565-579. https://doi.org/10.1007/s12145-019-00390-3
Qiu, Y. C., 2021. Design and Application of Knowledge Graph Visualization Platform(Dissertation). Beijing University of Posts and Telecommunications: Beijing(in Chinese with English abstract).
Tian, L., Zhang, J. C., Zhang, J. H., et al., 2021. Knowledge Graph Survey: Representation, Construction, Reasoning and Knowledge Hypergraph Theory. Journal of Computer Applications, 41(8): 2161-2186(in Chinese with English abstract).
Wang, C. B., Wang, M. G., Wang, B., et al., 2024. Knowledge Graph-Infused Quantitative Mineral Resource Forecasting. Earth Science Frontiers, 31(4): 26-36(in Chinese with English abstract).
Wang, C., 2021. Extraction of Spatiotemporal Attributes Information of Gold Mines and Visual Expression of Knowledge Graphs for Chinese Literature(Dissertation). Hefei University of Technology, Hefei(in Chinese with English abstract).
Wang, H., Zhang, Q. Q., Cai, W. W., et al., 2017. Research on Storage Method for Domain Ontology Based on Neo4j. Application Research of Computers, 34(8): 2404-2407(in Chinese with English abstract).
Wang, N. Y., Ye, Y. X., Liu, L., et al., 2021. Language Models Based on Deep Learning: a Review. Journal of Software, 32(4): 1082-1115(in Chinese with English abstract).
Wang, Q., Huang, J., Liu, X., et al., 2022. Construction and Visualization of Knowledge Graph of Geological Report. Computer Systems and Applications, 31(8): 140-145(in Chinese with English abstract).
Wu, R. Z., Li, H., Mei, H. B., et al., 2025. A Landslide Monitoring and Early Warning System with Retrieval-Augmented Generation Enhanced by Knowledge Graph. Earth Science, 50(10): 4125-4136(in Chinese with English abstract).
Wu, Z. W., Huang, B. X., Zhao, X. L., et al., 2024. Occurrence Characteristics and Distribution of Paragenetic Commercial Minerals and Associated Minerals in Coal-Bearing Strata in China. Journal of Mining & Safety Engineering, 41(1): 29-46(in Chinese with English abstract).
Xie, T., Yang, J. A., Liu, H., 2020. Chinese Entity Recognition Based on BERT-BiLSTM-CRF Model. Computer Systems & Applications, 29(7): 48-55(in Chinese with English abstract).
Xu, H. R., Xiang, Y., Ding, L., et al., 2025. Chinese Named Entity Recognition Method Based on Knowledge Enhancement from Large Language Models and Multi-feature Fusion. Journal of Beijing University of Aeronautics and Astronautics, 1-12 https://doi.org/10.13700/j.bh.1001-5965.2024.0421(in Chinese).
Yang, H., Sheng, Y. H., Wen, Y. N., et al., 2008. Research on Standardization and Formalization of Heterogeneous Geo-Analysis Models in Web Space. Geo-Information Science, 10(3): 382-389(in Chinese with English abstract).
Yang, J., 2020. Design and Implementation of Application Performance Management System Based on APM(Dissertation). Nanjing University, Nanjing(in Chinese with English abstract).
Yang, X. Z., 2016. Research and Implementation on Entity Alignment and Attribute Alignment(Dissertation). Beijing Institute of Technology, Beijing(in Chinese with English abstract).
Ye, Y. X., Liu, J. W., Zeng, W. X., et al., 2024. Ontology-Guided Knowledge Graph Construction for Mineral Prediction. Earth Science Frontiers, 31(4): 16-25(in Chinese with English abstract).
Yin, Q., Zhou, Y., Rao, J. Y., 2025. An Intelligent Prediction Method for Surface Settlement of Shield Tunnel Construction Based on CNN-BiLSTM-SA. KSCE Journal of Civil Engineering, 29(4): 100061. https://doi.org/10.1016/j.kscej.2024.100061
Yu, F., Wang, M. G., Wang, C. B., et al., 2025. GeoMinLM: a Large Language Model in Geology and Mineral Survey in Yunnan Province. Ore Geology Reviews, 182: 106638. https://doi.org/10.1016/j.oregeorev.2025.106638
Zhang, B. Y., Tang, J. C., Zhang, T. Y., et al., 2025. Knowledge Graph and Question-Answering Model for Geological Prospecting Empowered by Large Language Models. Earth Science. 1-24[2025-10-10]. https://doi.org/10.3799/dqkx.2025.176(in Chinese with English abstract).
Zhang, L., Lobov, A., 2024. Semantic Web Rule Language-Based Approach for Implementing Knowledge-Based Engineering Systems. Advanced Engineering Informatics, 62: 102587. https://doi.org/10.1016/j.aei.2024.102587
Zhang, Q. L., Zhou, Y. Z., Guo, L. X., et al., 2024. Intelligent Application of Knowledge Graphs in Mineral Prospecting: a Case Study of Porphyry Copper Deposits in the Qin-Hang Metallogenic Belt. Earth Science Frontiers, 31(4): 7-15(in Chinese with English abstract).
Zhao, P. D., 2019. Characteristics and Rational Utilization of Geological Big Data. Earth Science Frontiers, 26(4): 1-5(in Chinese with English abstract).
Zhao, T., Liu, C., Li, H. M., et al., 2025. Innovative Applications of Data Mining and Knowledge Graph Construction in National Mineral Resources Survey. Earth Science Frontiers, 1-14[2025-07-16]. https://doi.org/10.13745/j.esf.sf.2024.11.66(in Chinese with English abstract).
Zhou, C. H., Wang, H., Wang, C. S., et al., 2021. Geoscience Knowledge Graph in the Big Data Era. Science China Earth Sciences, 64(7): 1105-1114. https://doi.org/10.1007/s11430-020-9750-4
Zhou, C. H., Wang, H., Wang, C. S., et al., 2021. Research on Geo-Knowledge Map in the Age of Big Data. Scientia Sinica (Terrae), 51(7): 1070-1079(in Chinese with English abstract).
Zhou, Y. Z., Xiao, F., 2024. Overview: a Glimpse of the Latest Advances in Artificial Intelligence and Big Data Geoscience Research. Earth Science Frontiers, 31(4): 1-6(in Chinese with English abstract).
Zhou, Y. Z., Zhang, Q. L., Huang, Y. J., et al., 2021. Constructing Knowledge Graph for the Porphyry Copper Deposit in the Qingzhou-Hangzhou Bay Area: Insight into Knowledge Graph Based Mineral Resource Prediction and Evaluation. Earth Science Frontiers, 28(3): 67-75(in Chinese with English abstract).
Zhuang, Z. H., Jiao, S. L., Sun, C. H., 2024. Named Entity Recognition in Geological Field Based on BERT-BiLSTM-CRF. Computer & Digital Engineering, 52(6): 1815-1820, 1876(in Chinese with English abstract).
Zuo, R. G., 2021. Data Science-Based Theory and Method of Quantitative Prediction of Mineral Resources. Earth Science Frontiers, 28(3): 49-55(in Chinese with English abstract).
陈伟, 吴友政, 陈文亮, 等, 2018. 基于BiLSTM-CRF的关键词自动抽取. 计算机科学, 45(S1): 91-96, 113.
鄂海红, 张文静, 肖思琪, 等, 2019. 深度学习实体关系抽取研究综述. 软件学报, 30(6): 1793-1818.
韩伟, 张照坤, 雷辛亚, 等, 2025. 多源融合的"岩石-矿物-光谱"知识图谱构建及应用. 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, 2024. 我国共伴生矿产资源特点及综合评价要点综述. 中国矿业, 33(12): 17-23.
李冬梅, 张扬, 李东远, 等, 2020. 实体关系抽取方法研究综述. 计算机研究与发展, 57(7): 1424-1448.
廖振鑫, 2021. 面向地质领域的知识图谱构建研究及应用(硕士学位论文). 成都: 电子科技大学.
邱宇辰, 2021. 知识图谱可视化平台的设计实现及应用(硕士学位论文). 北京: 北京邮电大学.
田玲, 张谨川, 张晋豪, 等, 2021. 知识图谱综述: 表示、构建、推理与知识超图理论. 计算机应用, 41(8): 2161-2186.
汪陈, 2021. 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(硕士学位论文). 合肥: 合肥工业大学.
王成彬, 王明果, 王博, 等, 2024. 融合知识图谱的矿产资源定量预测. 地学前缘, 31(4): 26-36.
王红, 张青青, 蔡伟伟, 等, 2017. 基于Neo4j的领域本体存储方法研究. 计算机应用研究, 34(8): 2404-2407.
王乃钰, 叶育鑫, 刘露, 等, 2021. 基于深度学习的语言模型研究进展. 软件学报, 32(4): 1082-1115.
王晴, 黄进, 刘鑫, 等, 2022. 成果地质资料知识图谱构建与可视化. 计算机系统应用, 31(8): 140-145.
吴润泽, 李浩, 梅红波, 等, 2025. 基于知识图谱检索增强生成的滑坡监测预警系统. 地球科学, 50(10): 4125-4136. doi: 10.3799/dqkx.2025.127
吴占伟, 黄炳香, 赵兴龙, 等, 2024. 我国煤系共伴生矿产赋存特征及分布规律. 采矿与安全工程学报, 41(1): 29-46.
谢腾, 杨俊安, 刘辉, 2020. 基于BERT-BiLSTM-CRF模型的中文实体识别. 计算机系统应用, 29(7): 48-55.
徐浩然, 向阳, 丁玲, 2025. 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. 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.
杨军, 2020. 基于APM的应用性能管理系统的设计与实现(硕士学位论文). 南京: 南京大学.
杨秀璋, 2016. 实体和属性对齐方法的研究与实现(硕士学位论文). 北京: 北京理工大学.
叶育鑫, 刘家文, 曾婉馨, 等, 2024. 基于本体指导的矿产预测知识图谱构建研究. 地学前缘, 31(4): 16-25.
张宝一, 唐嘉成, 张彤蕴, 等, 2025. 大语言模型赋能的地质找矿知识图谱与问答模型构建. 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, 2024. 找矿知识图谱的智能化应用: 以钦杭成矿带斑岩铜矿为例. 地学前缘, 31(4): 7-15.
赵鹏大, 2019. 地质大数据特点及其合理开发利用. 地学前缘, 26(4): 1-5.
赵汀, 刘超, 李厚民, 等, 2025. 矿产资源国情调查中数据挖掘创新应用与知识图谱构建. 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, 2021. 大数据时代的地学知识图谱研究. 中国科学: 地球科学, 51(7): 1070-1079.
周永章, 肖凡, 2024. 管窥人工智能与大数据地球科学研究新进展. 地学前缘, 31(4): 1-6.
周永章, 张前龙, 黄永健, 等, 2021. 钦杭成矿带斑岩铜矿知识图谱构建及应用展望. 地学前缘, 28(3): 67-75.

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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. ORCID：0009-0008-1683-943X. E-mail：qinying@cumt.edu.cn

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杨慧，ORCID：0000-0001-9421-3573. E-mail：yanghui@cumt.edu.cn

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

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留言板

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. ORCID：0009-0008-1683-943X. E-mail：qinying@cumt.edu.cn

通讯作者: 杨慧，ORCID：0000-0001-9421-3573. E-mail：yanghui@cumt.edu.cn

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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. ORCID：0009-0008-1683-943X. E-mail：qinying@cumt.edu.cn

通讯作者:
杨慧，ORCID：0000-0001-9421-3573. E-mail：yanghui@cumt.edu.cn