Journal of Systems Engineering and Electronics ›› 2024, Vol. 35 ›› Issue (3): 679-688.doi: 10.23919/JSEE.2024.000046

• SYSTEMS ENGINEERING • Previous Articles    

How to implement a knowledge graph completeness assessment with the guidance of user requirements

Ying ZHANG(), Gang XIAO()   

  • Received:2023-05-23 Online:2024-06-18 Published:2024-06-19
  • 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, 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 the National University of Defense Technology. He is currently a professor at the National Key Laboratory for Complex Systems Simulation. His research interests include complex system theory, system design, knowledge graphs, and deep learning. E-mail:
  • Supported by:
    This work was supported by the National Key Laboratory for Complex Systems Simulation Foundation (6142006190301).


In the context of big data, many large-scale knowledge graphs have emerged to effectively organize the explosive growth of web data on the Internet. To select suitable knowledge graphs for use from many knowledge graphs, quality assessment is particularly important. As an important thing of quality assessment, completeness assessment generally refers to the ratio of the current data volume to the total data volume. When evaluating the completeness of a knowledge graph, it is often necessary to refine the completeness dimension by setting different completeness metrics to produce more complete and understandable evaluation results for the knowledge graph. However, lack of awareness of requirements is the most problematic quality issue. In the actual evaluation process, the existing completeness metrics need to consider the actual application. Therefore, to accurately recommend suitable knowledge graphs to many users, it is particularly important to develop relevant measurement metrics and formulate measurement schemes for completeness. In this paper, we will first clarify the concept of completeness, establish each metric of completeness, and finally design a measurement proposal for the completeness of knowledge graphs.

Key words: knowledge graph completeness assessment, relative completeness, user requirement, quality management