Neural Knowledge Base Repairs

Thomas Pellissier Tanon, Fabian Suchanek

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 [23], 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.

Original languageEnglish
Title of host publicationThe Semantic Web - 18th International Conference, ESWC 2021, Proceedings
EditorsRuben Verborgh, Katja Hose, Heiko Paulheim, Pierre-Antoine Champin, Maria Maleshkova, Oscar Corcho, Petar Ristoski, Mehwish Alam
PublisherSpringer Science and Business Media Deutschland GmbH
Pages287-303
Number of pages17
ISBN (Print)9783030773847
DOIs
Publication statusPublished - 1 Jan 2021
Event18th European Semantic Web Conference, ESWC 2021 - Virtual, Online
Duration: 6 Jun 202110 Jun 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12731 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th European Semantic Web Conference, ESWC 2021
CityVirtual, Online
Period6/06/2110/06/21

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