Wikidata completeness profiling using ProWD

Avicenna Wisesa, Fariz Darari, Adila Krisnadhi, Werner Nutt, Simon Razniewski

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Citations (Scopus)


Completeness is a crucial data quality aspect that deals with the question: do we have all the data we need? The lack of awareness on the completeness state of a knowledge graph (KG) may result in bias or even falsity for any decisions made based on the KG. Given a KG, one may be wondering how its completeness may vary across different topics. In this paper, we present ProWD, a framework and tool for profiling the completeness of Wikidata, a central KG on the (Semantic) Web that is open and free to use. ProWD measures the degree of completeness based on the Class-Facet-Attribute (CFA) profiles. A class denotes a collection of entities, which can be of multiple facets, allowing attribute completeness to be analyzed and compared, e.g., how does the completeness of the attribute "educated at" and "date of birth" compare between male, German computer scientists, and female, Indonesian computer scientists? ProWD generates summaries and visualizations for such analysis, giving insights into the KG completeness. ProWD is available online at∼\url

Original languageEnglish
Title of host publicationK-CAP 2019 - Proceedings of the 10th International Conference on Knowledge Capture
PublisherAssociation for Computing Machinery, Inc
Number of pages8
ISBN (Electronic)9781450370080
Publication statusPublished - 23 Sept 2019
Event10th International Conference on Knowledge Capture, K-CAP 2019 - Marina Del Rey, United States
Duration: 19 Nov 201921 Nov 2019

Publication series

NameK-CAP 2019 - Proceedings of the 10th International Conference on Knowledge Capture


Conference10th International Conference on Knowledge Capture, K-CAP 2019
Country/TerritoryUnited States
CityMarina Del Rey


  • Data completeness
  • Data profiling
  • Rdf
  • Sparql
  • Wikidata


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