Construction of Knowledge Graphs for Maritime Dangerous Goods

Qi Zhang, Yuanqiao Wen, Chunhui Zhou, Hai Long, Dong Han, Fan Zhang and Changshi Xiao
Additional contact information
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
Chunhui Zhou: School of Navigation, Wuhan University of 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
Changshi Xiao: School of Navigation, Wuhan University of Technology, Wuhan 430063, China

Sustainability, 2019, vol. 11, issue 10, 1-16

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.

Keywords: knowledge graph; maritime dangerous goods; ontology; knowledge representation; knowledge management (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://www.mdpi.com/2071-1050/11/10/2849/pdf (application/pdf)
https://www.mdpi.com/2071-1050/11/10/2849/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:11:y:2019:i:10:p:2849-:d:232503

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().