Volume 50 Issue 6
Jun.  2024
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Article Navigation >  Journal of Mine Automation  > 2024  >  50(6): 54-60
LUO Xiangyu, DU Hao, HUA Ying, et al. A method for constructing a knowledge graph of coal mine roof disaster prevention and control[J]. Journal of Mine Automation，2024，50（6）：54-60.  doi: 10.13272/j.issn.1671-251x.2023120032
Citation: LUO Xiangyu, DU Hao, HUA Ying, et al. A method for constructing a knowledge graph of coal mine roof disaster prevention and control[J]. Journal of Mine Automation，2024，50（6）：54-60.  doi: 10.13272/j.issn.1671-251x.2023120032
shu

A method for constructing a knowledge graph of coal mine roof disaster prevention and control

doi: 10.13272/j.issn.1671-251x.2023120032
  • 1.

    College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710600, China

  • 2.

    Key Laboratory of Western Mine Exploitation and Hazard Prevention, Ministry of Education, Xi'an University of Science and Technology, Xi'an 710054, China

  • 3.

    School of Energy Engineering, Xi'an University of Science and Technology, Xi'an 710054, China

  • Received Date: 2023-12-10
  • Rev Recd Date: 2024-05-31
    • Available Online: 2024-06-20
    Abstract
  • At present, the decision-making of coal mine roof disaster prevention and control measures and the analysis of accident causes mainly rely on manual experience, and the level of intelligence is relatively low. The knowledge graph of roof disaster prevention and control can integrate knowledge and experience of roof disaster prevention and control, assist in analyzing the causes of roof disaster accidents and making decisions on roof disaster prevention and control measures. A method for constructing a knowledge graph of coal mine roof disaster prevention and control has been proposed. The ontology method is used to complete the knowledge modeling of coal mine roof disaster prevention and control. The concepts in the field of roof disaster prevention and control are divided into mine geology, mining technology, prevention and control measures, and accident characterization. The relationships between concepts are defined as usage, triggering, susceptibility, control, prevention, and applicability. The knowledge modeling lays the foundation for the knowledge extraction of coal mine roof disaster prevention and control (entity extraction and relationship extraction). Based on the characteristics of entity overlapping between a large number of nested entities and relationships in the field of coal mine roof disaster prevention and control, a span based entity extraction method and a dependency syntax tree guided entity representation based relationship extraction method are determined. The method constructs a corpus in the field of roof disaster prevention and control, and uses the Neo4j graph database to store data, providing data source support for the application of knowledge graph of roof disaster prevention and control. The partial construction results of the knowledge graph of coal mine roof disaster prevention and control are displayed. It indicates that this knowledge graph can assist in the analysis of roof disaster accident causes and decision-making of prevention and control measures, thereby improving the intelligence level of roof management. It is pointed out that based on this knowledge graph, combined with natural language processing and knowledge reasoning technologies, knowledge Q&A on roof management can be achieved.

     

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    • References(30)
  • [1]
    康红普,张镇,黄志增. 我国煤矿顶板灾害的特点及防控技术[J]. 煤矿安全,2020,51(10):24-33,38.

    KANG Hongpu,ZHANG Zhen,HUANG Zhizeng. Characteristics of roof disasters and controlling techniques of coal mine in China[J]. Safety in Coal Mines,2020,51(10):24-33,38.
    [2]
    付恩三,白润才,刘光伟,等. “十三五”期间我国煤矿事故特征及演变趋势分析[J]. 中国安全科学学报,2022,32(12):88-94.

    FU Ensan,BAI Runcai,LIU Guangwei,et al. Analysis on characteristics and evolution trend of coal mine accidents in our country during "13th five-year" plan period[J]. China Safety Science Journal,2022,32(12):88-94.
    [3]
    赵亚军,张志男,贾廷贵. 2010—2021年我国煤矿安全事故分析及安全对策研究[J]. 煤炭技术,2023,42(8):128-131.

    ZHAO Yajun,ZHANG Zhinan,JIA Tinggui. Analysis of coal mine safety accidents and research on safety countermeasures in China from 2010 to 2021[J]. Coal Technology,2023,42(8):128-131.
    [4]
    徐刚,黄志增,范志忠,等. 工作面顶板灾害类型、监测与防治技术体系[J]. 煤炭科学技术,2021,49(2):1-11.

    XU Gang,HUANG Zhizeng,FAN Zhizhong,et al. Types,monitoring and prevention technology system of roof disasters in mining face[J]. Coal Science and Technology,2021,49(2):1-11.
    [5]
    王国法. 煤矿智能化最新技术进展与问题探讨[J]. 煤炭科学技术,2022,50(1):1-27. doi: 10.3969/j.issn.0253-2336.2022.1.mtkxjs202201001

    WANG Guofa. New technological progress of coal mine intelligence and its problems[J]. Coal Science and Technology,2022,50(1):1-27. doi: 10.3969/j.issn.0253-2336.2022.1.mtkxjs202201001
    [6]
    秦兵文,谢福星. 采场顶板灾变机理及预警系统[J]. 煤矿安全,2018,49(5):124-127.

    QIN Bingwen,XIE Fuxing. Disaster mechanism and early warning system of stope roof[J]. Safety in Coal Mines,2018,49(5):124-127.
    [7]
    丁震,李浩荡,张庆华. 煤矿灾害智能预警架构及关键技术研究[J]. 工矿自动化,2023,49(4):15-22.

    DING Zhen,LI Haodang,ZHANG Qinghua. Research on intelligent hazard early warning architecture and key technologies for coal mine[J]. Journal of Mine Automation,2023,49(4):15-22.
    [8]
    王文广. 知识图谱:认知智能理论与实战[M]. 北京:电子工业出版社,2022:4-7.

    WANG Wenguang. Knowledge graph:theory and practice of cognitive intelligence[M]. Beijing:Publishing House of Electronics Industry,2022:4-7.
    [9]
    张吉祥,张祥森,武长旭,等. 知识图谱构建技术综述[J]. 计算机工程,2022,48(3):23-37.

    ZHANG Jixiang,ZHANG Xiangsen,WU Changxu,et al. Survey of knowledge graph construction techniques[J]. Computer Engineering,2022,48(3):23-37.
    [10]
    付雷杰,曹岩,白瑀,等. 国内垂直领域知识图谱发展现状与展望[J]. 计算机应用研究,2021,38(11):3201-3214.

    FU Leijie,CAO Yan,BAI Yu,et al. Development status and prospect of vertical domain knowledge graph in China[J]. Application Research of Computers,2021,38(11):3201-3214.
    [11]
    ABU-SALIH B. Domain-specific knowledge graphs:a survey[J]. Journal of Network and Computer Applications,2021,185. DOI: 10.1016/j.jnca.2021.103076.
    [12]
    JI Shaoxiong,PAN Shirui,CAMBRIA E,et al. A survey on knowledge graphs:representation,acquisition,and applications[J]. IEEE Transactions on Neural Networks and Learning Systems,2021,33(2):494-514.
    [13]
    刘鹏,叶帅,舒雅,等. 煤矿安全知识图谱构建及智能查询方法研究[J]. 中文信息学报,2020,34(11):49-59.

    LIU Peng,YE Shuai,SHU Ya,et al. Coalmine safety:knowledge graph construction and its QA approach[J]. Journal of Chinese Information Processing,2020,34(11):49-59.
    [14]
    曹现刚,张梦园,雷卓,等. 煤矿装备维护知识图谱构建及应用[J]. 工矿自动化,2021,47(3):41-45.

    CAO Xiangang,ZHANG Mengyuan,LEI Zhuo,et al. Construction and application of knowledge graph for coal mine equipment maintenance[J]. Industry and Mine Automation,2021,47(3):41-45.
    [15]
    蔡安江,张妍,任志刚. 煤矿综采设备故障知识图谱构建[J]. 工矿自动化,2023,49(5):46-51.

    CAI Anjiang,ZHANG Yan,REN Zhigang. Fault knowledge graph construction for coal mine fully mechanized mining equipment[J]. Journal of Mine Automation,2023,49(5):46-51.
    [16]
    陈德彦,赵宏,张霞. 专家视图与本体视图的语义映射方法[J]. 软件学报,2020,31(9):2855-2882.

    CHEN Deyan,ZHAO Hong,ZHANG Xia. Semantic mapping methods between expert view and ontology view[J]. Journal of Software,2020,31(9):2855-2882.
    [17]
    吴炳潮,邓成龙,关贝,等. 动态迁移实体块信息的跨领域中文实体识别模型[J]. 软件学报,2022,33(10):3776-3792.

    WU Bingchao,DENG Chenglong,GUAN Bei,et al. Dynamically transfer entity span information for cross-domain Chinese named entity recognition[J]. Journal of Software,2022,33(10):3776-3792.
    [18]
    WANG Yu,TONG Hanghang,ZHU Ziye,et al. Nested named entity recognition:a survey[J]. ACM Transactions on Knowledge Discovery from Data,2022,16(6):1-29.
    [19]
    宁尚明,滕飞,李天瑞. 基于多通道自注意力机制的电子病历实体关系抽取[J]. 计算机学报,2020,43(5):916-929.

    NING Shangming,TENG Fei,LI Tianrui. Multi-channel self-attention mechanism for relation extraction in clinical records[J]. Chinese Journal of Computers,2020,43(5):916-929.
    [20]
    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(6):1793-1818.

    E Haihong,ZHANG Wenjing,XIAO Siqi,et al. Survey of entity relationship extraction based on deep learning[J]. Journal of Software,2019,30(6):1793-1818.
    [21]
    ZHU Huiming,HE Chunhui,FANG Yang,et al. Fine grained named entity recognition via seq2seq framework[J]. IEEE Access,2020,8:53953-53961. doi: 10.1109/ACCESS.2020.2980431
    [22]
    YANG Dongying,LIAN Tao,ZHENG Wen,et al. Enriching word information representation for Chinese cybersecurity named entity recognition[J]. Neural Processing Letters,2023,55(6):7689-7707. doi: 10.1007/s11063-023-11280-7
    [23]
    ZHENG Changmeng,CAI Yi,XU Jingyun,et al. A boundary-aware neural model for nested named entity recognition[C]. Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing,Hong Kong,2019:357-366.
    [24]
    TAN Chuanqi,QIU Wei,CHEN Mosha,et al. Boundary enhanced neural span classification for nested named entity recognition[C]. The AAAI Conference on Artificial Intelligence,New York,2020:9016-9023.
    [25]
    LI Fei,WANG Zheng,HUI Siucheung,et al. A segment enhanced span-based model for nested named entity recognition[J]. Neurocomputing,2021,465:26-37. doi: 10.1016/j.neucom.2021.08.094
    [26]
    TANG Ruixue,CHEN Yanping,QIN Yongbin,et al. Boundary regression model for joint entity and relation extraction[J]. Expert Systems with Applications,2023,229. DOI: 10.1016/J.ESWA.2023.120441.
    [27]
    ZHENG Suncong,WANG Feng,BAO Hongyun,et al. Joint extraction of entities and relations based on a novel tagging scheme[C]. The 55th Annual Meeting of the Association for Computational Linguistics,Vancouver,2017:1227-1236.
    [28]
    WEI Zhepei,SU Jianlin,WANG Yue,et al. A novel cascade binary tagging framework for relational triple extraction[C]. The 58th Annual Meeting of the Association for Computational Linguistics,Tokyo,2020:1476-1488.
    [29]
    SHEIKHAEI M S,ZAFARI H,TIAN Yuan. Joined type length encoding for nested named entity recognition[J]. Transactions on Asian and Low-Resource Language Information Processing,2021,21(3):1-23.
    [30]
    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(8):2161-2186.

    TIAN Ling,ZHANG Jinchuan,ZHANG Jinhao,et al. Knowledge graph survey:representation,construction,reasoning and knowledge hypergraph theory[J]. Journal of Computer Applications,2021,41(8):2161-2186.
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