IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i22p14975-d970792.html
   My bibliography  Save this article

ERSDMM: A Standard Digitalization Modeling Method for Emergency Response Based on Knowledge Graph

Author

Listed:
  • Wenling Liu

    (School of Management, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Yuexiang Yang

    (School of Management, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Xinyu Tu

    (School of Management, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Wan Wang

    (Sub-Institute of Public Safety Standardization, China National Institute of Standardization, Beijing 100191, China)

Abstract

Standard digitalization is a crucial step in social and economic development and the transformation of digital technology. Standard digitalization is of great significance in the promotion of sustainable economic and social development. This paper proposes a standard digitalization modeling method for emergency response (ERSDMM) based on knowledge graph (KG). Firstly, this paper analyzes the knowledge structure of emergency response standards (ERS) and constructs a “seven-dimensional” model of ERS based on the public safety triangle theory. An ontology model of the emergency response domain is then created. Secondly, ERS and emergency scenario fine-grained knowledge are extracted. Thirdly, a standard reorganization model is constructed to meet the needs of the scenario response. Finally, the ERSDMM is applied to the GB 21734-2008, which proves that the ERSDMM is available. Taking RES as an example, this paper explores the path and practice of standard digitalization. ERSDMM solves standards-related problems, such as overlapping content, coarse knowledge granularity, incomplete coverage of elements, and difficulty in acquiring knowledge.

Suggested Citation

  • Wenling Liu & Yuexiang Yang & Xinyu Tu & Wan Wang, 2022. "ERSDMM: A Standard Digitalization Modeling Method for Emergency Response Based on Knowledge Graph," Sustainability, MDPI, vol. 14(22), pages 1-18, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:14975-:d:970792
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/22/14975/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/22/14975/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Qi Zhang & Yuanqiao Wen & Chunhui Zhou & Hai Long & Dong Han & Fan Zhang & Changshi Xiao, 2019. "Construction of Knowledge Graphs for Maritime Dangerous Goods," Sustainability, MDPI, vol. 11(10), pages 1-16, May.
    2. Jianzhuo Yan & Tiantian Lv & Yongchuan Yu, 2018. "Construction and Recommendation of a Water Affair Knowledge Graph," Sustainability, MDPI, vol. 10(10), pages 1-15, September.
    3. Filippos Lygerakis & Nikos Kampelis & Dionysia Kolokotsa, 2022. "Knowledge Graphs’ Ontologies and Applications for Energy Efficiency in Buildings: A Review," Energies, MDPI, vol. 15(20), pages 1-32, October.
    4. Yukun Jiang & Xin Gao & Wenxin Su & Jinrong Li, 2021. "Systematic Knowledge Management of Construction Safety Standards Based on Knowledge Graphs: A Case Study in China," IJERPH, MDPI, vol. 18(20), pages 1-15, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yukun Jiang & Xin Gao & Wenxin Su & Jinrong Li, 2021. "Systematic Knowledge Management of Construction Safety Standards Based on Knowledge Graphs: A Case Study in China," IJERPH, MDPI, vol. 18(20), pages 1-15, October.
    2. Qi Zhang & Yuanqiao Wen & Chunhui Zhou & Hai Long & Dong Han & Fan Zhang & Changshi Xiao, 2019. "Construction of Knowledge Graphs for Maritime Dangerous Goods," Sustainability, MDPI, vol. 11(10), pages 1-16, May.
    3. Laihao Ma & Xiaoxue Ma & Jingwen Zhang & Qing Yang & Kai Wei, 2021. "Identifying the Weaker Function Links in the Hazardous Chemicals Road Transportation System in China," IJERPH, MDPI, vol. 18(13), pages 1-17, July.
    4. Andrej David & Peter Mako & Jan Lizbetin & Patrik Bohm, 2021. "The Impact of an Environmental Way of Customer’s Thinking on a Range of Choice from Transport Routes in Maritime Transport," Sustainability, MDPI, vol. 13(3), pages 1-23, January.
    5. Jiyuan Tan & Qianqian Qiu & Weiwei Guo & Tingshuai Li, 2021. "Research on the Construction of a Knowledge Graph and Knowledge Reasoning Model in the Field of Urban Traffic," Sustainability, MDPI, vol. 13(6), pages 1-20, March.
    6. Akeem Pedro & Anh-Tuan Pham-Hang & Phong Thanh Nguyen & Hai Chien Pham, 2022. "Data-Driven Construction Safety Information Sharing System Based on Linked Data, Ontologies, and Knowledge Graph Technologies," IJERPH, MDPI, vol. 19(2), pages 1-18, January.
    7. Ru-Guan Wang & Wen-Jen Ho & Kuei-Chun Chiang & Yung-Chieh Hung & Jen-Kuo Tai & Jia-Cheng Tan & Mei-Ling Chuang & Chi-Yun Ke & Yi-Fan Chien & An-Ping Jeng & Chien-Cheng Chou, 2023. "Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph Techniques," Energies, MDPI, vol. 16(19), pages 1-24, September.
    8. Jongmo Kim & Kunyoung Kim & Mye Sohn & Gyudong Park, 2022. "Deep Model-Based Security-Aware Entity Alignment Method for Edge-Specific Knowledge Graphs," Sustainability, MDPI, vol. 14(14), pages 1-22, July.
    9. Qi He & Chenyang Yu & Wei Song & Xiaoyi Jiang & Lili Song & Jian Wang, 2023. "ISLKG: The Construction of Island Knowledge Graph and Knowledge Reasoning," Sustainability, MDPI, vol. 15(17), pages 1-26, September.
    10. Danling Yuan & Keping Zhou & Chun Yang, 2023. "Architecture and Application of Traffic Safety Management Knowledge Graph Based on Neo4j," Sustainability, MDPI, vol. 15(12), pages 1-26, June.
    11. Ziwei Xiao & Chunxiao Zhang, 2021. "Construction of Meteorological Simulation Knowledge Graph Based on Deep Learning Method," Sustainability, MDPI, vol. 13(3), pages 1-20, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:14975-:d:970792. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.