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Neural Knowledge Base Repairs

Abstract : The curation of a knowledge base is a crucial but costly task. In this work, we suggest to make use of the advances in neural network research to improve the automated correction of constraint violations. Our method is a deep learning refinement of "Learning how to correct a knowledge base from the edit history", and similarly uses the edits that solved some violations in the past to infer how to solve similar violations in the present. Our system makes use of the graph content, literal embeddings, and features extracted from Web pages to improve its performance. The experimental evaluation on Wikidata shows significant improvements over baselines.
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Contributor : Thomas Pellissier Tanon Connect in order to contact the contributor
Submitted on : Tuesday, August 24, 2021 - 1:18:27 PM
Last modification on : Tuesday, September 21, 2021 - 2:16:03 PM


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Thomas Pellissier Tanon, Fabian Suchanek. Neural Knowledge Base Repairs. European Semantic Web Conference, Jun 2021, Hersonissos (virtual), Greece. pp.287-303, ⟨10.1007/978-3-030-77385-4_17⟩. ⟨hal-03325101⟩



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