Journal of Systems Engineering and Electronics ›› 2024, Vol. 35 ›› Issue (1): 154-162.doi: 10.23919/JSEE.2023.000150

• SYSTEMS ENGINEERING • Previous Articles    

Classification of knowledge graph completeness measurement techniques

Ying ZHANG(), Gang XIAO()   

  1. 1 Institute of Systems Engineering, Academy of Military Sciences, Beijing 100107, China
  • Received:2022-12-11 Online:2024-02-18 Published:2024-03-05
  • Contact: Ying ZHANG;
  • About author:
    ZHANG Ying was born in 1996. She received her M.S. degree in data science from King ’s College of London (KCL), British, in 2020. She is working toward her Ph.D. degree in the Institute of Systems Engineering, Academy of Military Sciences. Her research interests include knowledge graph refinement, and knowledge graph evaluation. E-mail:

    XIAO Gang was born in 1973. He received his B.S. and Ph.D. degrees in computer science and technology from National University of Defense Technology. He is currently a professor of National Key Laboratory for Complex Systems Simulation. His research interests include complex system theory, system design, knowledge graph, and deep learning. E-mail:
  • Supported by:
    This work was supported by the National Key Laboratory for Complex Systems Simulation Foundation (6142006190301).


At present, although knowledge graphs have been widely used in various fields such as recommendation systems, question and answer systems, and intelligent search, there are always quality problems such as knowledge omissions and errors. Quality assessment and control, as an important means to ensure the quality of knowledge, can make the applications based on knowledge graphs more complete and more accurate by reasonably assessing the knowledge graphs and fixing and improving the quality problems at the same time. Therefore, as an indispensable part of the knowledge graph construction process, the results of quality assessment and control determine the usefulness of the knowledge graph. Among them, the assessment and enhancement of completeness, as an important part of the assessment and control phase, determine whether the knowledge graph can fully reflect objective phenomena and reveal potential connections among entities. In this paper, we review specific techniques of completeness assessment and classify completeness assessment techniques in terms of closed world assumptions, open world assumptions, and partial completeness assumptions. The purpose of this paper is to further promote the development of knowledge graph quality control and to lay the foundation for subsequent research on the completeness assessment of knowledge graphs by reviewing and classifying completeness assessment techniques.

Key words: quality assessment, completeness assessment, closed world assumptions, open world assumption, partial completeness assumption