中国安全科学学报 ›› 2024, Vol. 34 ›› Issue (5): 28-35.doi: 10.16265/j.cnki.issn1003-3033.2024.05.0835

• 安全社会科学与安全管理 • 上一篇    下一篇

基于知识图谱的煤矿建设安全领域知识管理研究

许娜1(), 梁燕翔1,**(), 王亮2, 赵丽丽3, 周雪晴1, 张博4   

  1. 1 中国矿业大学 力学与土木工程学院,江苏 徐州 221116
    2 中国矿业大学 安全工程学院, 江苏 徐州 221116
    3 上海勘测设计研究院有限公司,上海 200434
    4 中国矿业大学 计算机科学与技术学院,江苏 徐州 221116
  • 收稿日期:2023-11-21 修回日期:2024-02-22 出版日期:2024-05-28
  • 通讯作者:
    **梁燕翔(1998—),女,江苏盐城人,硕士研究生,主要研究方向为深度学习、知识图谱等。E-mail:
  • 作者简介:

    许 娜 (1982—),女,江苏徐州人,博士,副教授,主要从事煤炭安全、基础设施工程项目管理、大数据与人工智能技术等方面的研究。E-mail:

    王亮 教授

    张博 副教授

  • 基金资助:
    国家社会科学基金资助(23BGL277); 江苏省社科基金面上项目资助(22GLB023); 中国矿业大学研究生创新计划项目(2023WLJCRCZL062)

Research on knowledge management in coal mine construction safety field based on knowledge graph

XU Na1(), LIANG Yanxiang1,**(), WANG Liang2, ZHAO Lili3, ZHOU Xueqing1, ZHANG Bo4   

  1. 1 School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou Jiangsu 221116, China
    2 School of Safety Engineering, China University of Mining and Technology, Xuzhou Jiangsu 221116, China
    3 Shanghai Investigation, Design & Research Institute Co., Ltd., Shanghai 200434, China
    4 School of Computer Science and Technology, China University of Mining and Technology, Xuzhou Jiangsu 221116, China
  • Received:2023-11-21 Revised:2024-02-22 Published:2024-05-28

摘要:

为解决煤矿建设过程中数据积累存在的知识冗余现象,研究基于知识图谱(KG)的安全领域知识管理。从安全管理系统结构和安全隐患风险管理2个维度,系统化分析领域标准规范文本,界定煤矿建设安全管理领域的12类实体类型和10种关系类型,完善知识结构模式;选取领域43部标准规范为数据源,引入规则、机器学习法、深度学习法识别文本实体和关系;针对不同实体类型,提出领域知识综合方法框架,并对比分析双向长短期记忆(BiLSTM)和条件随机场(CRF)与双向编码器表示(BERT)-BiLSTM-CRF模型。研究结果表明:BERT-BiLSTM-CRF模型在准确率、召回率和F1值方面均比BiLSTM-CRF模型高出7%,验证了所选模型的优越性和准确性;通过知识抽取、知识存储及可视化等过程,挖掘出煤矿建设安全领域不同实体类型所包含的实体和不同实体间的关系。

关键词: 知识图谱(KG), 煤矿建设, 安全领域, 安全管理, 知识结构, 实体类型

Abstract:

In order to solve the knowledge redundancy caused by data accumulation during coal mine construction, some researches were conducted about domain knowledge management based on KG in this paper. The systematic analysis of the field of standard specification text was conducted using the safety management system structure and security risk management. 12 types of entities and 10 types of relations in the field of coal mine construction safety management were defined, and the knowledge structure model was also improved. 43 standard specifications were selected as data sources for text entity and relationship recognition by rules, dictionaries and deep learning methods. For different entity types, the framework of domain knowledge integrated approaches was proposed, and two models of bidirectional long-short-term memory(BiLSTM) and conditional random field(CRF) and bidirectional encoder representations from transformers(BERT)-BiLSTM-CRF were also compared. The accuracy, recall rate and F1 value of the BERT-BiLSTM-CRF model are more than 7% higher than that of the BiLSTM-CRF model, which verifies the superiority of the selected model. Through knowledge extraction, knowledge storage and visualization, the entities contained in different types of entities in the field of coal mine construction safety and the relationship between different entities were investigated.

Key words: knowledge graph(KG), coal mine construction, safety field, safety management, knowledge structure, entity types

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