@inproceedings{9df9e849a7e241219e097732a726081b,
title = "Learning how to correct a knowledge base from the edit history",
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.",
keywords = "Data cleaning, History, Knowledge base, Rule mining, Wikidata",
author = "Tanon, \{Thomas Pellissier\} and Camille Bourgaux and Fabian Suchanek",
note = "Publisher Copyright: {\textcopyright} 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License.; 2019 World Wide Web Conference, WWW 2019 ; Conference date: 13-05-2019 Through 17-05-2019",
year = "2019",
month = may,
day = "13",
doi = "10.1145/3308558.3313584",
language = "English",
series = "The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019",
publisher = "Association for Computing Machinery, Inc",
pages = "1465--1475",
booktitle = "The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019",
}