[1]黄海来,宋瑞.地铁运营故障知识图谱构建方法[J].长安大学学报(自然科学版),2025,45(5):172-185.[doi:10.19721/j.cnki.1671-8879.2025.05.015]
　HUANG Hai-lai,SONG Rui.Construction method of metro operation fault knowledge graph[J].Journal of Chang’an University (Natural Science Edition),2025,45(5):172-185.[doi:10.19721/j.cnki.1671-8879.2025.05.015]
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地铁运营故障知识图谱构建方法()
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长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
第45卷
期数:
2025年5期
页码:
172-185
栏目:
交通工程
出版日期:
2025-09-30

文章信息/Info

Title:
Construction method of metro operation fault knowledge graph
文章编号:
1671-8879(2025)05-0172-14
作者:
黄海来1; 2; 宋瑞1
(1. 北京交通大学 交通运输学院,北京 100044; 2. 上海申通地铁集团有限公司,上海 201103)
Author(s):
HUANG Hai-lai1; 2;  SONG Rui1
(1. School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China; 2. Shanghai Shentong Metro Group Co., Ltd., Shanghai 201103, China)
关键词:
交通工程;  地铁运营故障;  自然语言处理;  知识图谱;  故障辅助决策
Keywords:
Key words:traffic engineering;  metro operation fault;  natural language processing;  knowledge graph;  fault aided decision
分类号:
U298.5
DOI:
10.19721/j.cnki.1671-8879.2025.05.015
文献标志码:
A
摘要:
地铁运营故障知识大量沉没于地铁运营企业的各类非结构化安全文本中,难以复用,为有效提取高质量故障信息并对其开展更深层次解析,提出一种基于自然语言处理技术及深度学习的地铁运营故障知识图谱构建方法,其查询及分析结果可为地铁故障处置及预防提供更全面的数据支撑及决策支持。首先,自顶向下建立地铁运营故障的本体模型框架,采用面向语言理解的广义自回归预训练(XLNet)、双向门控循环单元(BiGRU)和自注意力机制(Self-Attention)相结合的文本分类算法和预训练语言模型抽取地铁安全文本中的故障描述段落。然后,基于双向编码器转换器(BERT)-双向长短期记忆网络(BiLSTM)-条件随机场(CRF)模型进一步提取描述段落中的故障实体及关系以构建知识图谱,并分别对比提取实体采用的BERT-BiLSTM-CRF模型、BiLSTM-CRF与BiLSTM模型在中国某一线城市地铁运营企业运营安全文本语料上的试验情况。采用提出方法构建地铁运营故障知识图谱,并基于实例开展图谱查询。研究结果表明:BERT-BiLSTM-CRF模型在地铁故障实体抽取任务上实现了较高的综合性能,精确率和召回率的调和平均数F1达到了0.75,而在线路实体识别方面,F1达到了0.98; 提出方法可从地铁运营安全文本中有效获取地铁运营故障有关知识并构建知识图谱,进而开展关联关系的查询,充分挖掘难以经人工分析获取的与故障有关的知识,实现地铁安全文本的高水平数字化分析。
Abstract:
A substantial corpus of metro operation fault knowledge is recognized as being dispersed throughout the unstructured safety texts of metro operation enterprises and is difficult to be reused. To address this challenge, a method for constructing a metro operation fault knowledge graph based on natural language processing technology and deep learning was proposed. This approach was designed to facilitate the extraction of high-quality fault information and enable a more in-depth analysis, thereby providing a valuable resource for metro fault disposal and prevention. First, a top-down ontological model framework for metro operation faults was established, and a text classification algorithm combining the generalized autoregressive pretraining for language understanding(XLNet), bidirectional gated recurrent unit(BiGRU), and self-attention mechanism, along with a pre-trained language model, was employed to extract fault description paragraphs from metro safety texts. Then, based on the bidirectional encoder representations from transformers(BERT)-bidirectional long short term memory networks(BiLSTM)-conditional random field(CRF)model, fault entities and relationships within the description paragraphs were further extracted to construct a knowledge graph. The experimental results of the BERT-BiLSTM-CRF model, BiLSTM-CRF model, and BiLSTM model for entity extraction were compared using operation safety text corpora from a metro operation enterprise in a first-tier city in China. The proposed method was used to construct a knowledge graph of metro operation failures, and graph queries were conducted based on instances. The research results demonstrate that the BERT-BiLSTM-CRF model achieves high comprehensive performance in the task of metro fault entity extraction, with an average the harmonic mean of precision and recall(F1)reaching to 0.75, while for line entity recognition, the F1 reaches to 0.98. The proposed method can effectively acquire knowledge related to metro operation failures from metro safety texts and construct a knowledge graph, thereby enabling the query of associated relationships. This fully explores fault-related knowledge that is difficult to obtain through manual analysis, achieving high-level digital analysis of metro safety texts.4 tabs, 11 figs, 36 refs.

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备注/Memo

备注/Memo:
收稿日期:2025-02-28
基金项目:国家自然科学基金项目(62076023)
作者简介:黄海来(1988-),女,江西九江人,上海申通地铁集团有限公司高级工程师,北京交通大学工程博士研究生,E-mail:170243328@qq.com。
通信作者:宋 瑞(1971-),女,河北景县人,教授,博士研究生导师,E-mail:rsong@bjtu.edu.cn。
更新日期/Last Update: 2025-09-30