IDEAS home Printed from https://ideas.repec.org/h/spr/lnopch/978-981-99-2625-1_23.html
   My bibliography  Save this book chapter

Research on Knowledge Graph Platform of Logistics Industry Based on Big Data

In: Liss 2022

Author

Listed:
  • Fan Yang

    (Beijing Wuzi University, Beijing Key Laboratory of Intelligent Logistics Systems)

  • Juntao Li

    (Beijing Wuzi University, Beijing Key Laboratory of Intelligent Logistics Systems)

  • Ruiping Yuan

    (Beijing Wuzi University, Beijing Key Laboratory of Intelligent Logistics Systems)

  • Fan Wang

    (Beijing Wuzi University, Beijing Key Laboratory of Intelligent Logistics Systems)

  • Huanli Zhao

    (Beijing Wuzi University, Beijing Key Laboratory of Intelligent Logistics Systems)

Abstract

China's logistics industry started late, with the rapid development of the national economy, China's logistics industry maintains a fast growth rate, and the data generated is growing explosively. Data and knowledge are the basis for the deep integration of new-generation information technology and intelligent manufacturing. How to efficiently and accurately analyze multi-source and heterogeneous data in various systems of the logistics industry is a major problem faced by people in the logistics industry. The knowledge graph is a new type of accurate knowledge representation method, which has gradually begun to land in medical, financial, and other industries. For the characteristics of the logistics industry, this paper conducts special research on five aspects, including knowledge acquisition, knowledge processing, knowledge graphing, knowledge application, and knowledge reasoning. It aims to promote the application of the knowledge graph platform in the logistics industry and provide decision-making support for auxiliary event-driven and industry-important events.

Suggested Citation

  • Fan Yang & Juntao Li & Ruiping Yuan & Fan Wang & Huanli Zhao, 2023. "Research on Knowledge Graph Platform of Logistics Industry Based on Big Data," Lecture Notes in Operations Research, in: Xiaopu Shang & Xiaowen Fu & Yixuan Ma & Daqing Gong & Juliang Zhang (ed.), Liss 2022, pages 305-313, Springer.
    • Handle: RePEc:spr:lnopch:978-981-99-2625-1_23
    DOI: 10.1007/978-981-99-2625-1_23
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:lnopch:978-981-99-2625-1_23. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.