Learning How to Correct a Knowledge Base from the Edit History

Thomas Pellissier Tanon (1) , Camille Bourgaux (2) , Fabian M. Suchanek (1)

1 LTCI - Laboratoire Traitement et Communication de l'Information
2 VALDA - Value from Data

The curation of a knowledge base is a crucial but costly task. In this work, we propose to take advantage of the edit history of the knowledge base in order to learn how to correct constraint violations. Our method is based on rule mining, and uses the edits that solved some violations in the past to infer how to solve similar violations in the present. The experimental evaluation of our method on Wikidata shows significant improvements over baselines.

Keywords: Data cleaning; Rule mining; Wikidata; History; Knowledge base

Domains: Artificial Intelligence [cs.AI]; Databases [cs.DB]

Thomas Pellissier Tanon, Camille Bourgaux, Fabian M. Suchanek. Learning How to Correct a Knowledge Base from the Edit History. World Wide Web Conference, May 2019, San Francisco, United States. ⟨10.1145/3308558.3313584⟩. ⟨hal-02066041⟩