Knowledge gap discovery: A case study of Wikidata

Millenio Ramadizsa, Fariz Darari, Werner Nutt, Simon Razniewski

Research output: Contribution to journalConference articlepeer-review


Society, science, and economy are becoming more and more data-driven, and therefore the study of gaps in knowledge gains importance. The arguably most prominent public source of structured knowledge is Wikidata, which contains impressive amounts of knowledge, but nonetheless comes with surprising gaps. In this paper we propose a framework for identifying class-level knowledge gaps in Wikidata, based on the concepts of gap properties, i.e., properties that mostly exist for prominent entities, but are missing in the tail, and the gap property ratio. We conduct an analysis for a varied set of 20 classes, and show that our framework can discover unexpected knowledge gaps, that may guide contributors towards addressing them.

Original languageEnglish
JournalCEUR Workshop Proceedings
Publication statusPublished - 2023
Event4th Wikidata Workshop, Wikidata 2023 - Athens, Greece
Duration: 13 Nov 2023 → …

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