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Construction of Knowledge Graphs for Maritime Dangerous Goods

Author

Listed:
  • Qi Zhang

    (School of Navigation, Wuhan University of Technology, Wuhan 430063, China)

  • Yuanqiao Wen

    (Intelligent Transportation System Research Centre, Wuhan University of Technology, Wuhan 430063, China
    National Engineering Research Centre for Water Transport Safety, Wuhan 430063, China
    Hubei Key Laboratory of Inland Shipping Technology, Wuhan 430063, China)

  • Chunhui Zhou

    (School of Navigation, Wuhan University of Technology, Wuhan 430063, China
    National Engineering Research Centre for Water Transport Safety, Wuhan 430063, China
    Hubei Key Laboratory of Inland Shipping Technology, Wuhan 430063, China)

  • Hai Long

    (Institute of Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, 04107 Leipzig, Germany)

  • Dong Han

    (School of Navigation, Wuhan University of Technology, Wuhan 430063, China)

  • Fan Zhang

    (School of Navigation, Wuhan University of Technology, Wuhan 430063, China
    National Engineering Research Centre for Water Transport Safety, Wuhan 430063, China
    Hubei Key Laboratory of Inland Shipping Technology, Wuhan 430063, China)

  • Changshi Xiao

    (School of Navigation, Wuhan University of Technology, Wuhan 430063, China
    National Engineering Research Centre for Water Transport Safety, Wuhan 430063, China
    Hubei Key Laboratory of Inland Shipping Technology, Wuhan 430063, China)

Abstract

Dangerous goods occupy an important proportion in international shipping, and government and enterprises pay a lot of attention to transport safety. There are a wide variety of dangerous goods, and the knowledge involved is extensive and complex. Organizing and managing this knowledge plays an important role in the safe transportation of dangerous goods. The knowledge graph is a mass of brand-new knowledge management technologies that provide powerful technical support for integrating domain knowledge and solving the problem of the “knowledge island.” This paper first introduces the knowledge of maritime dangerous goods (MDG); constructs a three-layer knowledge structure of MDG, dividing this knowledge into two categories; uses ontology to express the concepts, entities, and relations of MDG; and puts forward the representation methods of the conceptual layer and entity layer and designs them in detail. Finally, the knowledge graph of maritime dangerous goods (KGMDG) is constructed. Furthermore, we demonstrate the knowledge visualization, retrieval, and automatic judgment of segregation requirement based on KGMDG. It is proved that KGMDG does not only help to simplify the retrieval process of professional knowledge and to promote intelligent transportation but is also conducive to the sharing, dissemination, and utilization of MDG knowledge.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:10:p:2849-:d:232503
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    References listed on IDEAS

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    Cited by:

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    3. 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.
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    5. 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.
    6. 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.
    7. 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.

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