首页|Classification of knowledge graph completeness measurement techniques
Classification of knowledge graph completeness measurement techniques
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NETL
NSTL
万方数据
维普
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 pro-cess,the results of quality assessment and control determine the usefulness of the knowledge graph.Among them,the assessment and enhancement of completeness,as an impor-tant part of the assessment and control phase,determine whether the knowledge graph can fully reflect objective pheno-mena and reveal potential connections among entities.In this paper,we review specific techniques of completeness assess-ment and classify completeness assessment techniques in terms of closed world assumptions,open world assumptions,and par-tial 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.
quality assessmentcompleteness assessmentclosed world assumptionsopen world assumptionpartial com-pleteness assumption
ZHANG Ying、XIAO Gang
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Institute of Systems Engineering,Academy of Military Sciences,Beijing 100107,China
National Key Laboratory for Complex Systems Simulation Foundation
NETL
NSTL
万方数据
维普
