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Knowledge Modeling and Analysis for Railway Fire Accident Using Ontology-Based Knowledge Graph

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Proceedings of the 5th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2021 (EITRT 2021)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 867))

Abstract

As a typical scene of railway accidents, railway fire hazard will lead to heavy losses and serious social impact once it happens. However, there are many factors causing the disaster of railway fire and the coupling relationship among the factors is complicated. Therefore, an ontology-based knowledge graph construction and analysis method is proposed. First, based on FAR accident data of American railways, the construction method of railway accident ontology and its relationship is studied, and the pattern layer of railway accident knowledge graph is established. Second, the correlation and importance analysis methods of railway fire accidents are studied, combining with the causative mechanism of fire accidents, a multi-dimensional fusion model is established to extract the fire entities. Third, Neo4j is used to construct the knowledge graph of railway fire accidents. Finally, the railway fire accident knowledge graph constructed in this paper is applied and verified to realize the accurate positioning of the key factors of railway fire accidents and the accurate query of their paths, which is of great significance for the identification, prevention and control of the key risk points and risk paths of railway fire accidents. The construction and application of this knowledge graph will provide knowledge support for railway fire risk prevention and control, and facilitate the “proactive” transformation of railway safety prevention and control.

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Acknowledgements

This work is supported by the Fundamental Research Funds for the Central Universities (No.2020JBZD011); The National Natural Science Foundation of China (No. 61903023); the Natural Science Foundation of Beijing Municipality (No. 4204110); State Key Laboratory of Rail Traffic Control and Safety (No. RCS2020ZT006, RCS2021ZT006); the Fundamental Research Funds for the Central Universities (No. 2020JBM087).

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Authors and Affiliations

  1. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, 100044, China

    Han Yan, Xiaoping Ma, Fei Chen, Ruhao Zhao & Limin Jia

  2. School of Transportation and Traffic, Beijing Jiaotong University, Beijing, 100044, China

    Han Yan, Xiaoping Ma, Fei Chen, Ruhao Zhao & Limin Jia

  3. Key lab of Proactive Safety and Risk Control in Railway Operation, Beijing Jiaotong University, Beijing, 100044, China

    Xiaoping Ma & Limin Jia

Authors
  1. Han Yan
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  2. Xiaoping Ma
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  3. Fei Chen
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  4. Ruhao Zhao
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  5. Limin Jia
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Corresponding author

Correspondence to Xiaoping Ma .

Editor information

Editors and Affiliations

  1. CRRC Qingdao Sifang Co., Ltd., Qingdao, Shandong, China

    Jianying Liang

  2. Beijing Jiaotong University, Beijing, Beijing, China

    Limin Jia

  3. Beijing Jiaotong University, Beijing, Beijing, China

    Yong Qin

  4. Beijing Jiaotong University, Beijing, Beijing, China

    Zhigang Liu

  5. Beijing Jiaotong University, Beijing, Beijing, China

    Lijun Diao

  6. University of Salford, Salford, UK

    Min An

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Yan, H., Ma, X., Chen, F., Zhao, R., Jia, L. (2022). Knowledge Modeling and Analysis for Railway Fire Accident Using Ontology-Based Knowledge Graph. In: Liang, J., Jia, L., Qin, Y., Liu, Z., Diao, L., An, M. (eds) Proceedings of the 5th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2021. EITRT 2021. Lecture Notes in Electrical Engineering, vol 867. Springer, Singapore. https://doi.org/10.1007/978-981-16-9909-2_59

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  • DOI: https://doi.org/10.1007/978-981-16-9909-2_59

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-9908-5

  • Online ISBN: 978-981-16-9909-2

  • eBook Packages: EngineeringEngineering (R0)

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