基于知识图谱的深地数据智能检索算法实现

doi:10.3799/dqkx.2026.158

基于知识图谱的深地数据智能检索算法实现

doi: 10.3799/dqkx.2026.158

1. 中国地质大学(武汉）信息化工作办公室, 湖北武汉 430074;
2. 航天宏图信息技术股份有限公司武汉分公司, 湖北武汉 430073;
3. 武汉蓝天绿野咨询设计有限公司, 湖北武汉 430024;
4. 航天宏图信息技术股份有限公司, 北京 100195;
5. 湖北溪岸节能环保科技有限公司, 湖北武汉 430056

详细信息

作者简介:
杨强根(1973-),男,高级工程师,博士,从事GIS以及网络信息技术应用研究,ORCID:0000-0001-9215-8128,E-mail:yangqg@cug.edu.cn

通讯作者:
刘洪光(1985-),男,高级工程师,硕士,从事GIS、自然灾害监测预警风险评估以及信息技术应用研究,ORCID:0009-0004-8127-9054,E-mail:308287460@qq.com

施霞珍(1984-),女,高级工程师,硕士,从事生态保护及环境咨询行业,ORCID:0009-0003-5362-5522,E-mail:395649178@qq.com

中图分类号: PP542+.5
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出版历程
- 收稿日期: 2025-09-30
- 网络出版日期: 2026-06-29

Implementation of Knowledge Graph-Based Intelligent Retrieval Algorithm for Deep Geological Data

1. Network and Information Center, China University of Geosciences, Wuhan 430074, China;
2. Piesat Information Technology Co., Ltd. Wuhan Branch, Wuhan 430073, China;
3. Wu Han Lan Tian Lv Ye Consulting And Design Co., Ltd., Wuhan 430024, China;
4. Piesat Information Technology Co., Ltd., Beijing 100195, China;
5. Hubei Xian Energy Saving And Environmental Protection Technology Co., Ltd., Wuhan 430056, China

摘要

摘要: 深部地质数据是地球科学研究的基础性战略资源，在保障国家能源安全、防灾减灾、促进地质研究等方面起着重要作用。为了科学、有效地发挥深部地质数据的效能，对深部地质数据开展有效检索和智能检索，本研究引入知识图谱技术，结合深部地质数据特点，理清深部地质知识的关联关系，创新性地研究、实现深部地质数据智能检索算法。该算法在进行验证分析的同时，开展其在深地科学大数据管理与服务系统平台的应用。研究结果表明：本算法能够为深部地质智能检索业务提供有效的支持，显著提高深部地质检索业务的准确性和可靠性，为深部地质其他方面的业务应用提供了新的思路和方法。
- 知识图谱 /
- 本体 /
- 深部地质数据 /
- 智能检索 /
- 算法
Abstract: Deep geological data serves as a fundamental strategic resource for Earth science research, playing a crucial role in ensuring national energy security, disaster prevention and mitigation, and advancing geological studies. To scientifically and effectively utilize the potential of deep geological data, this study introduces knowledge graph technology to enable efficient and intelligent retrieval of such data. By analyzing the characteristics of deep geological data, we clarify the interrelationships of deep geological knowledge and innovatively research and implement an intelligent retrieval algorithm for deep geological data. While validating the algorithm, we also apply it to a deep Earth science big data management and service platform. The results demonstrate that this algorithm provides effective support for intelligent retrieval of deep geological data, significantly improving the accuracy and reliability of retrieval operations. Moreover, it offers new ideas and methods for other applications in deep geological research.
- knowledge graph /
- ontology /
- deep geological data /
- intelligent retrieval /
- algorithm

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参考文献(29)

[1]	Chen, X. H., Pan, M., Shi, Y. L., et al., 2017. Research on Semantic Relevance Calculation Based on Domain Constraint Ontology. Science Technology and Engineering, 17(19):187-189. doi: 10.3969/j.issn.1671-1815.2017.19.034
[2]	Duan, Y. X., Qiu, Q. J., Tian, M., et al., 2024. Geological map⁃oriented knowledge graph construction and intelligent Q&A application. Chinese Journal of Geology, 59(2):588⁃602. doi: 10.12017/dzkx.2024.041
[3]	Han, L.,Wang, L., Ding, X. M., et al., 2022. Similarity quantification of soil parametric data and sites using confidence ellipses. Geoscience Frontiers, 13(1):1-13. doi: 10.1016/j.gsf.2021.101280
[4]	He, H. X., 2023. Construction and application of earthquake knowledge Graph (A Thesis Submitted for the Degree of Master). Lanzhou: Lanzhou Jiaotong University.
[5]	Huang, H. Q., Yu, J., Liao, X., et al., 2019. Review on Knowledge Graphs. Computer Systems & Applications, 28(6):6-10. doi: 10.15888/j.cnki.csa.006915
[6]	Li, C., Mao, X., Lu, W. J., et al., 2024. Research on natural language place name query method. Science of Surveying and Mapping, 49(4):187-195. doi: 10.16251/j.cnki.1009-2307.2024.04.018
[7]	Liao, Z. X., 2021. The Construction And Application Of Knowledge Graph For Geology (A Thesis Submitted for the Degree of Master). Chengdu: University of Electronic Science and Technology of China.
[8]	Liu, D. S., Liu, H. L., Wu, Y., et al., 2022. Characterization of geo-material parameters: Gene concept and big data approach in geotechnical engineering, Geosystems and Geoenvironment, 1(1):1-9. doi: 10.1016/j.geogeo.2021.09.003
[9]	Liu, H. G., Yang, Q. G., Shi, X. Z., et al., 2025. Construction Methodology of the Modeling the Risk Assessment for Typhoon-Collapse Disaster Chain for Public Safety,Earth Science, 50(10):4060-4065. doi: 10.3799/dqkx.2025.153
[10]	Phoon, K. K., Zhang, W. G., 2023. Future of machine learning in geotechnics, Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 17(1), 7-22. doi: 10.1080/17499518.2022.2087884
[11]	Qi, H., 2020. The Construction of Ontology-based Earth Science Knowledge Graph (A Thesis Submitted for the Degree of Master). Nanjing: Nanjing University.
[12]	Qiu, Q. J., Wu, L., Ma, K., et al., 2023. A Knowledge Graph Construction Method for Geohazard Chain for Disaster Emergency Response. Earth Science, 48(5):1876-1887. doi: 10.3799/dqkx.2022.313
[13]	Xiao, K. Y., Li, C., Tang, R., et al., 2025. Big data intelligent prediction and evaluation. Earth Science Frontiers.doi: 10.13745/j.esf.sf.2025.4.58
[14]	Xu, X. R.,2024. Research on the Construction Method of Knowledge Graph of Complex Geological Structure Based on Knowledge Reasoning (A Thesis Submitted for the Degree of Master). Chengdu: University of Electronic Science and Technology of China.
[15]	Zhang, B. Y., 2023. Research on Geographic Data Recommendation Methods Based on Knowledge Graphs (A Thesis Submitted for the Degree of Master). Beijing: Beijing University of Civil Engineering and Architecture.
[16]	Zhang, H., 2021. Construction and Application of knowledge Graph in the Field of Debris Flow Disaster (A Thesis Submitted for the Degree of Master). Kunming: Yunnan Normal University.
[17]	陈小红,潘懋,史艳丽,等,2017.基于领域约束本体的语义相关度计算研究.科学技术与工程,17(19):187-189.
[18]	段雨希,邱芹军,田苗等,2024.面向地质图的知识图谱构建及智能问答应用.地质科学,59(2):588-602.
[19]	贺海霞,2023.地震灾害知识图谱构建与应用研究(硕士学位论文).兰州:兰州交通大学.
[20]	黄恒琪,于娟,廖晓,等,2019.知识图谱研究综述.计算机系统应用,28(6):6-10.
[21]	李诚,毛曦,路文娟,等,2024.基于知识图谱的自然语言式地名查询方法研究.测绘科学,49(4):187-195.
[22]	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.
[23]	刘洪光,杨强根,施霞珍,等,2025.面向公共安全的台风-崩塌灾害链风险评估模型构建方法.地球科学,50(10):4060-4065.
[24]	齐浩,2020.基于本体的地球科学知识图谱的构建(硕士学位论文).南京:南京大学.
[25]	邱芹军,吴亮,马凯,等,2023.面向灾害应急响应的地质灾害链知识图谱构建方法.地球科学,48(5):1876-1887.
[26]	肖克炎,李程,唐瑞,等,2025.大数据智能预测评价.地学前缘,32(4):2-15.
[27]	许鑫冉,2024.基于知识推理的复杂地质构造知识图谱构建方法研究(硕士学位论文).成都:电子科技大学.
[28]	张彬钰,2023.基于知识图谱的地理数据推荐方法研究(硕士学位论文).北京:北京建筑大学.
[29]	张航,2021.泥石流灾害领域知识图谱的构建与应用(硕士学位论文).昆明:云南师范大学.

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基于知识图谱的深地数据智能检索算法实现

doi: 10.3799/dqkx.2026.158

作者简介:
杨强根(1973-),男,高级工程师,博士,从事GIS以及网络信息技术应用研究,ORCID:0000-0001-9215-8128,E-mail:yangqg@cug.edu.cn

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Implementation of Knowledge Graph-Based Intelligent Retrieval Algorithm for Deep Geological Data

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留言板

基于知识图谱的深地数据智能检索算法实现

doi: 10.3799/dqkx.2026.158

作者简介: 杨强根(1973-),男,高级工程师,博士,从事GIS以及网络信息技术应用研究,ORCID:0000-0001-9215-8128,E-mail:yangqg@cug.edu.cn

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出版历程

Implementation of Knowledge Graph-Based Intelligent Retrieval Algorithm for Deep Geological Data

计量

出版历程

目录

作者简介:
杨强根(1973-),男,高级工程师,博士,从事GIS以及网络信息技术应用研究,ORCID:0000-0001-9215-8128,E-mail:yangqg@cug.edu.cn