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基于煤矿井下不安全行为知识图谱构建方法

付燕 刘致豪 叶鸥

付燕,刘致豪,叶鸥. 基于煤矿井下不安全行为知识图谱构建方法[J]. 工矿自动化,2024,50(1):88-95.  doi: 10.13272/j.issn.1671-251x.2023060014
引用本文: 付燕,刘致豪,叶鸥. 基于煤矿井下不安全行为知识图谱构建方法[J]. 工矿自动化,2024,50(1):88-95.  doi: 10.13272/j.issn.1671-251x.2023060014
FU Yan, LIU Zhihao, YE Ou. A method for constructing a knowledge graph of unsafe behaviors in coal mines[J]. Journal of Mine Automation,2024,50(1):88-95.  doi: 10.13272/j.issn.1671-251x.2023060014
Citation: FU Yan, LIU Zhihao, YE Ou. A method for constructing a knowledge graph of unsafe behaviors in coal mines[J]. Journal of Mine Automation,2024,50(1):88-95.  doi: 10.13272/j.issn.1671-251x.2023060014

基于煤矿井下不安全行为知识图谱构建方法

doi: 10.13272/j.issn.1671-251x.2023060014
基金项目: 中国博士后科学基金项目(2020M673446)。
详细信息
    作者简介:

    付燕(1972—) ,女,河南鹤壁人,教授,博士,主要研究方向为计算机图形图像处理技术、科学计算及其可视化技术等,E-mail:942542352@qq.com

    通讯作者:

    刘致豪(1997—),男,河南商丘人,硕士研究生,主要研究方向为知识图谱,E-mail:2267318289@qq.com

  • 中图分类号: TD79

A method for constructing a knowledge graph of unsafe behaviors in coal mines

  • 摘要: 虽然知识图谱已广泛应用于各个领域,但在煤矿安全方面,尤其在煤矿井下不安全行为方面的研究较少。构建了一种自底向上的煤矿井下不安全行为知识图谱。首先,采用传统机器学习和深度学习算法相结合的方法进行命名实体识别,采用RoBERTa进行词语向量化,采用双向长短时记忆网络(BiLSTM)对向量进行标注,提高网络模型对上下文特征的捕捉能力,通过多层感知机(MLP)解决煤矿井下不安全行为数据集数据量不足的问题,采用条件随机场(CRF)模型解决前面存在的单词关系不识别问题,并捕获全文信息和预测结果。其次,根据语句的结构特点,设计了基于知识“实体−关系−实体”三元组的依存句法树结构,对井下不安全行为领域的知识资源进行知识抽取与表示。最后,构建面向井下不安全行为的知识图谱。实验结果表明:① RoBERTa−BiLSTM−MLP−CRF模型对于导致结果、违反性行为、错误性行为及粗心性行为4类实体类别具有较好的识别效果,其准确率分别为86.7%,80.3%,80.7%,77.4%。② 在相同的数据集下,RoBERTa−BiLSTM−MLP−CRF模型训练的准确率、召回率、F1值较RoBERTa−BiLSTM−CRF模型分别提高了1.6%,1.5%,1.6%。

     

  • 图  1  基于RoBERTa−BiLSTM−MLP−CRF实体识别过程

    Figure  1.  RoBERTa-BiLSTM-MLP-CRF based entity recognition

    图  2  RoBERTa模型

    Figure  2.  RoBERTa model

    图  3  BiLSTM模型

    Figure  3.  BiLSTM model

    图  4  MLP模型

    Figure  4.  MLP model

    图  5  线性链CRF模型

    Figure  5.  Linear chain CRF model

    图  6  RoBERTa−BiLSTM−MLP−CRF模型

    Figure  6.  RoBERTa-BiLSTM-MLP-CRF model

    图  7  部分煤矿井下不安全行为知识图谱

    Figure  7.  Knowledge graph of underground unsafe behavior in some underground coal mines

    表  1  实体待预测标签

    Table  1.   Entity to be predicted labels

    实体类型 开始标签 中间或结尾标签
    遗忘性行为 B−forget I−forget
    粗心性行为 B−careless I−careless
    错误性行为 B−error I−error
    违反性行为 B−violate I−violate
    关联因素影响性行为 B−factor I− factor
    导致后果 B−cause I−cause
    下载: 导出CSV

    表  2  实体相似度计算实例

    Table  2.   Example of entity similarity calculation

    实体1实体2SconsineSJarccard
    粉尘瓦斯爆炸粉尘瓦斯事故0.670.50
    违章指挥违章命令0.670.60
    不安全动作不安全行为0.600.43
    安全培训安全训练0.670.60
    下载: 导出CSV

    表  3  基于Neo4j的知识存储方案

    Table  3.   Neo4j-based knowledge storage solutions

    类型作用对象范围
    节点描述知识实体井下扒车、穿化纤衣入井等
    标签描述知识概念类违章指挥、违规操作等
    描述实体关系包含关系、关联关系等
    下载: 导出CSV

    表  4  实体类型识别效果

    Table  4.   Entity type identification effect %

    实体类别 P R F1
    遗忘性行为 63.5 67.4 65.4
    粗心性行为 77.4 84.1 80.6
    错误性行为 80.7 83.1 81.9
    违反性行为 80.3 83.7 82.0
    关联因素影响性行为 73.0 76.0 74.5
    导致后果 86.7 90.0 88.3
    下载: 导出CSV

    表  5  模型对比结果

    Table  5.   Model contrast results %

    模型 P R F1
    BiLSTM−CRF 71.2 74.8 73.0
    BERT−BiLSTM−CRF 74.9 79.1 77.0
    RoBERTa−BiLSTM−CRF 75.6 79.1 77.3
    RoBERTa−BiLSTM−MLP−CRF 77.2 80.6 78.9
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-06-06
  • 修回日期:  2024-01-08
  • 网络出版日期:  2024-01-31

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