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Research Article | Publishing Language: Chinese | Open Access

Development analysis of mining subsidence research based on knowledge graph

College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
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Abstract

Focusing on mining subsidence research, this study conducted a comprehensive analysis using the CiteSpace citation network visualization tool based on literature from both the China National Knowledge Infrastructure (CNKI) and Web of Science (WoS) databases. By analyzing publication volume, research institutions, and keyword clusters, this study maps knowledge graphs of research institutions and keywords to uncover research hotspots and frontier trends in the field of mining-induced subsidence. The results indicate that there is a high level of attention from the academic community regarding subsidence issues; Chinese institutions dominate in terms of publication quantity, with China University of Mining and Technology leading with 1456 papers. Currently, both domestic and international research hotspots focus on high-precision monitoring technology and subsidence prediction and monitoring in complex environments. The integration of multi-source data has become a significant trend in mining subsidence monitoring and research.

CLC number: TD325 Document code: A Article ID: 2096-2193(2025)03-0399-09

References

[1]

HU Jian. The utilization status, supply and demand pattern, and development trend of global coal resources[J]. China Coal, 2024, 50(11): 153-162.

[2]

TENG Yongjia, YAN Yueguan, GUO Wei, et al. Line integral method for predicting surface subsidence in irregular working face mining[J]. Journal of Mining Science and Technology, 2022, 7(1): 82-88.

[3]

SUN Wenjie, LI Wenjie, YANG Wenkai, et al. Classification of water environment problems in coal mine and treatment mode[J]. Safety in Coal Mines, 2023, 54(5): 35-41.

[4]

CHEN Chao, HU Zhenqi. Current status and progress on the application of key stratum theory in mining subsidence[J]. Journal of Mining Science and Technology, 2017, 2(3): 209-218.

[5]

LIU Baochen, DAI Huayang. Research development and origin of probability integral method[J]. Coal Mining Technology, 2016(2): 1-3.

[6]

LI Jingyu, WANG Lei, JIANG Kegui, et al. Parameter inversion method of probability integral model based on improved wolves algorithm[J]. Journal of Mining and Strata Control Engineering, 2021, 3(1): 75-82.

[7]

LI Peixian, WAN Haoming, XU Yue, et al. Parameter inversion of probability integration method using surface movement vector[J]. Chinese Journal of Geotechnical Engineering, 2018, 40(4): 767-776.

[8]

MEI Han, CHEN Bingqian, WANG Zhengshuai, et al. Comparative study on the parameters of the inversion probability integral method with different intelligent optimization algorithms[J]. Metal Mine, 2021(5): 149-159.

[9]

ZHAN Huizhu, SHANG Hui, GAN Zhihui. Fusion method of high-resolution remote sensing data in coal mining subsidence area[J]. Journal of Xi'an University of Science and Technology, 2021, 41(4): 673-681.

[10]

YAO Wanqiang, MENG Yanbin, ZHENG Junliang, et al. Research on DEM multiple filtering method for mining subsidence[J]. Safety in Coal Mines, 2024, 55(1): 167-175.

[11]

YANG Zhengqing, LIU Zhenyu. The mining subsidence prediction and analysis system based on GIS[J]. Geotechnical Investigation & Surveying, 2019, 47(11): 41-44, 70.

[12]

TAN Zhixiang, YANG Jiawei, DENG Kazhong. Study on method of mining subsidence parameters calculating for whole basin of mining area based on SBAS-InSAR[J]. Coal Science and Technology, 2021, 49(1): 312-318.

[13]

HAO Dengcheng, WANG Guorui, LI Peixian, et al. Subsection Kalman filter model for mining subsidence monitoring data processing[J]. Journal of Mining Science and Technology, 2021, 6(4): 371-378.

[14]

PENG C Y, XIA F, NASERIPARSA M, et al. Knowledge graphs: opportunities and challenges[J]. Artificial Intelligence Review, 2023, 56(11): 13071-13102.

[15]

ECK N J V, WALTMAN L. Visualizing Bibliometric Networks[M]. Cham: Springer International Publishing, 2014: 285-320.

[16]

COBO M J, LóPEZ-HERRERA A G, HERRERA-VIEDMA E, et al. Science mapping software tools: Review, analysis, and cooperative study among tools[J]. Journal of the American Society for information Science and Technology, 2011, 62(7): 1382-1402.

[17]

WANG R, ZHU Y X, WANG Y. Knowledge graph and frontier trends in melanoma-associated ncRNAs: a bibliometric analysis from 2006 to 2023[J]. Frontiers in Oncology, 2024, 141439324-1439324.

[18]

PAN Yudai, ZHANG Lingling, CAI Zhongmin, et al. Differentiable rule extraction with large language model for knowledge graph reasoning[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(10): 2403-2412.

[19]

HE Jing, FENG Yuanliu, SHAO Jingwen. Research progress on multi-source data fusion based on CiteSpace[J]. Journal of Guangxi Normal University: Natural Science Edition, 2024, 42(5): 13-27.

[20]

KAUR A, KUMAR V, SINDHWANI R, et al. Public debt sustainability: a bibliometric co-citation visualization analysis[J]. International Journal of Emerging Markets, 2024, 19(4): 1090-1110.

[21]

ZENG Jingwei, JING Guoxun, ZHU Qifeng. Visualization analysis of current research situation in field of deep coal mining[J]. Journal of Mining Science and Technology, 2022, 7(6): 752-762.

[22]

ZHANG Min, MA Chenyang, ZHONG Chongwu. Literature visualization analysis on research trend of mining subsidence in China[J]. Shandong Coal Science and Technology, 2021, 39(6): 208-212.

[23]

BIAN He, ZHU Bingbing, LI Heng, et al. Bibliometric analysis of landscape connectivity research based on CiteSpace[J]. Chinese Agricultural Science Bulletin, 2023, 39(31): 157-164.

[24]

XIE Chen, PAN Rui. U. S. -Led Multilateral Export Controls on China: A Case Study of American-Led Chips Blockade against China[J]. The Chinese Journal of American Studies, 2023, 37(6): 126-157, 8.

[25]

XU Shiang, WU Haibo, OU Yuanchao, et al. Review of loose layer deformation under coal mining subsidence conditions[J]. Science Technology and Engineering, 2024, 24(17): 6999-7013.

[26]

MAO Guozheng, FANG Yuan, LI Xinsen, et al. Progress of the studies of mining subsidence: based on the literature statistics of CNKI journals from 2011 to 2021[J]. Modern Mining, 2022, 38(9): 53-57, 94.

[27]

TANG Fuquan, YANG Qian. Progress and prospects of multi-source remote sensing monitoring technology for coal mining subsidence in mining areas of the western Loess Plateau[J]. Coal Science and Technology, 2023, 51(12): 9-26.

Journal of Mining Science and Technology
Pages 399-407
Cite this article:
WANG Z. Development analysis of mining subsidence research based on knowledge graph. Journal of Mining Science and Technology, 2025, 10(3): 399-407. https://doi.org/10.19606/j.cnki.jmst.2025042

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Received: 23 February 2024
Revised: 15 May 2024
Published: 30 June 2025
© The Author(s) 2025

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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