主管：中华人民共和国应急管理部
主办：应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU
消防科学与技术

消防科学与技术 ›› 2023, Vol. 42 ›› Issue (5): 718-723.

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燃气事故应急处置知识图谱构建方法

齐子琛1,2, 胡玉玲1,2, 万雨瑞1,2, 卓亮3   

  1. （1. 北京建筑大学 电气与信息工程学院，北京 102616；2. 北京建筑大学 建筑大数据智能处理方法研究北京市重点实验室，北京 100044；3. 滁州中石油昆仑燃气有限公司，安徽 滁州 239400）
  • 出版日期:2023-05-15 发布日期:2023-05-15
  • 作者简介:齐子琛（1998- ），男，北京人，北京建筑大学电气与信息工程学院硕士研究生，主要从事燃气应急处置与知识图谱构建方法研究，北京市大兴区黄村地区永源路15号，102616。

Construction method of knowledge graph for gas accident emergency disposal

Qi Zichen1,2, Hu Yuling1,2, Wan Yurui1,2, Zhuo Liang3   

  1. (1. Institute of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102616, China; 2. Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing University of Civil Engineering and Architecture, Beijing 100044, China;3. PetroChina Kunlun Gas Co. Ltd. of Chuzhou, Anhui Chuzhou 239400, China)
  • Online:2023-05-15 Published:2023-05-15

摘要： 目前，燃气事故应急处置预案大多以文本形式存储，处置人员需要通过查阅大量文本确定相应的处置措施，难以满足应急事件处置的快速性与时效性需求。为了克服时效性差的缺点，及提高以文本形式存储的燃气应急处理预案的可复用性与可扩展性，构建了基于燃气事故应急处置知识图谱的辅助决策框架，采用“自顶向下”的方式构建知识图谱。采取BERT-BiLSTM-CRF模型对燃气事故的背景信息进行实体抽取，采取结合语义角色标注与依存句法分析的方法对燃气事故处置方法和处置信息进行实体-关系三元组抽取，最终在Neo4j图数据库当中存储与显示构建的燃气事故应急处置知识图谱。研究可为燃气事故应急处置辅助决策提供有效支持。

关键词: 燃气应急处置, 知识图谱, BERT-BiLSTM-CRF模型, 依存句法分析, 语义角色标注

Abstract: Currently, most of the gas accident emergency response plans are stored in the form of text, and the on-site disposal personnel need to check a large number of texts to determine the corresponding disposal measures, which can hardly meet the rapidity and timeliness of emergency response. In order to overcome the shortcomings of poor timeliness and improve the reusability and flexibility of the gas emergency response plan stored in the form of text, an auxiliary decision-making framework based on the knowledge graph of gas accident emergency response was built, using a "top-down" approach. BERT-BiLSTM-CRF method is used to extract the background information of gas accident cases, and a method combining semantic role labeling with dependency parsing is proposed to build entity-relationship triad. The extracted gas emergency disposal entities and relational knowledge are finally stored and displayed in the Neo4j graph database. This study can provide effective support for auxiliary decision-making of gas accident emergency disposal.

Key words: gas emergency disposal, knowledge graph, BERT-BiLSTM-CRF model, dependency parsing, semantic role labeling