TY - JOUR
T1 - Knowledge gap discovery
T2 - 4th Wikidata Workshop, Wikidata 2023
AU - Ramadizsa, Millenio
AU - Darari, Fariz
AU - Nutt, Werner
AU - Razniewski, Simon
N1 - Publisher Copyright:
© 2023 Copyright for this paper by its authors.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85185882149&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85185882149
SN - 1613-0073
VL - 3640
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
Y2 - 13 November 2023
ER -