Abstract : 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.
https://hal-imt.archives-ouvertes.fr/hal-02066041
Contributor : Thomas Pellissier Tanon
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Submitted on : Wednesday, March 13, 2019 - 10:23:57 AM
Last modification on : Thursday, December 19, 2019 - 1:32:11 AM
Long-term archiving on: Friday, June 14, 2019 - 12:51:23 PM
Thomas Tanon, Camille Bourgaux, Fabian 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⟩
