Knowledge Graph Completeness: A Systematic Literature Review

Subhi Issa*, Onaopepo Adekunle, Faycal Hamdi, Samira Si-Said Cherfi, Michel Dumontier, Amrapali Zaveri

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Web of Science)

Abstract

The quality of a Knowledge Graph (also known as Linked Data) is an important aspect to indicate its fitness for use in an application. Several quality dimensions are identified, such as accuracy, completeness, timeliness, provenance, and accessibility, which are used to assess the quality. While many prior studies offer a landscape view of data quality dimensions, here we focus on presenting a systematic literature review for assessing the completeness of Knowledge Graph. We gather existing approaches from the literature and analyze them qualitatively and quantitatively. In particular, we unify and formalize commonly used terminologies across 56 articles related to the completeness dimension of data quality and provide a comprehensive list of methodologies and metrics used to evaluate the different types of completeness. We identify seven types of completeness, including three types that were not previously identified in previous surveys. We also analyze nine different tools capable of assessing Knowledge Graph completeness. The aim of this Systematic Literature Review is to provide researchers and data curators a comprehensive and deeper understanding of existing works on completeness and its properties, thereby encouraging further experimentation and development of new approaches focused on completeness as a data quality dimension of Knowledge Graph.

Original languageEnglish
Pages (from-to)31322-31339
Number of pages18
JournalIEEE Access
Volume9
DOIs
Publication statusPublished - 2021

Keywords

  • Data integrity
  • Linked data
  • Systematics
  • Measurement
  • Bibliographies
  • Tools
  • Search problems
  • Assessment
  • completeness
  • data quality
  • KG
  • knowledge graph
  • linked data
  • LOD
  • metrics
  • survey
  • systematic literature review

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