research-article

Learning How to Correct a Knowledge Base from the Edit History

Authors:

Thomas Pellissier Tanon,

Camille Bourgaux,

Fabian SuchanekAuthors Info & Claims

WWW '19: The World Wide Web Conference

Pages 1465 - 1475

https://doi.org/10.1145/3308558.3313584

Published: 13 May 2019 Publication 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.

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Cited By

Hao SChai CLi GTang NWang NYu X(2023)HOFD: An Outdated Fact Detector for Knowledge BasesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.324822335:10(10775-10789)Online publication date: 1-Oct-2023
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cover image ACM Other conferences

WWW '19: The World Wide Web Conference

May 2019

3620 pages

ISBN:9781450366748

DOI:10.1145/3308558

Editors:
Ling Liu
Georgia Tech, USA
,
Ryen White
Microsoft Research, USA

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 13 May 2019

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May 13 - 17, 2019

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Cited By

Hao SChai CLi GTang NWang NYu X(2023)HOFD: An Outdated Fact Detector for Knowledge BasesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.324822335:10(10775-10789)Online publication date: 1-Oct-2023
https://doi.org/10.1109/TKDE.2023.3248223
Pelgrin OTaelman RGalárraga LHose K(2023)Scaling Large RDF Archives To Very Long Histories2023 IEEE 17th International Conference on Semantic Computing (ICSC)10.1109/ICSC56153.2023.00013(41-48)Online publication date: Feb-2023
https://doi.org/10.1109/ICSC56153.2023.00013
Yang XWang N(2023)A confidence-aware and path-enhanced convolutional neural network embedding framework on noisy knowledge graphNeurocomputing10.1016/j.neucom.2023.126261545(126261)Online publication date: Aug-2023
https://doi.org/10.1016/j.neucom.2023.126261
Khoo FMark MTimmer RMartins MFoshee EBugbee KRenard GBerea A(2023)Building Knowledge Graphs in Heliophysics and AstrophysicsNatural Language Processing and Information Systems10.1007/978-3-031-35320-8_15(215-228)Online publication date: 14-Jun-2023
https://doi.org/10.1007/978-3-031-35320-8_15
Chen JJiménez-Ruiz EHorrocks IChen XMyklebust E(2022)An assertion and alignment correction framework for large scale knowledge basesSemantic Web10.3233/SW-21044814:1(29-53)Online publication date: 30-Nov-2022
https://doi.org/10.3233/SW-210448
Xue BZou L(2022)Knowledge Graph Quality Management: a Comprehensive SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3150080(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3150080
Pelgrin OGalárraga LHose KSaleem MVerborgh RAli MHartig O(2021)Towards fully-fledged archiving for RDF datasetsSemantic Web10.3233/SW-21043412:6(903-925)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/SW-210434
Bleifus TBornemann LKalashnikov DNaumann FSrivastava D(2021)Structured Object Matching across Web Page Revisions2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00115(1284-1295)Online publication date: Apr-2021
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Pellissier Tanon TSuchanek F(2021)Neural Knowledge Base RepairsThe Semantic Web10.1007/978-3-030-77385-4_17(287-303)Online publication date: 6-Jun-2021
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