Negative Knowledge for Open-world Wikidata

Abstract

The Wikidata knowledge base (KB) is one of the most popular structured data repositories on the web, containing more than 1 billion statements for over 90 million entities. Like most major KBs, it is nonetheless incomplete and therefore operates under the open-world assumption (OWA)-statements not contained in Wikidata should be assumed to have an unknown truth. The OWA ignores however, that a significant part of interesting knowledge is negative, which cannot be readily expressed in this data model. In this paper, we review the challenges arising from the OWA, as well as some specific attempts Wikidata has made to overcome them. We review a statistical inference method for negative statements, called peer-based inference, and present Wikinegata, a platform that implements this inference over Wikidata. We discuss lessons learned from the development of this platform, as well as how the platform can be used both for learning about interesting negations, as well as about modelling challenges inside Wikidata. Wikinegata is available at https://d5demos.mpi-inf.mpg.de/negation.