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Correcting Knowledge Base Assertions

dc.contributor.authorChen, Jiaoyan
dc.contributor.authorChen, Xi
dc.contributor.authorHorrocks, Ian
dc.contributor.authorJimenez-Ruiz, Ernesto
dc.contributor.authorMyklebust, Erik Bryhn
dc.date.accessioned2021-05-21T10:28:58Z
dc.date.available2021-05-21T10:28:58Z
dc.date.created2020-11-09T11:35:28Z
dc.date.issued2020
dc.identifier.citationWWW '20: Proceedings of The Web Conference 2020en_US
dc.identifier.isbn978-1-4503-7023-3
dc.identifier.urihttps://hdl.handle.net/11250/2756012
dc.description.abstractThe usefulness and usability of knowledge bases (KBs) is often limited by quality issues. One common issue is the presence of erroneous assertions, often caused by lexical or semantic confusion. We study the problem of correcting such assertions, and present a general correction framework which combines lexical matching, semantic embedding, soft constraint mining and semantic consistency checking. The framework is evaluated using DBpedia and an enterprise medical KB.en_US
dc.language.isoengen_US
dc.publisherAssociation for Computing Machinery (ACM), 2020.en_US
dc.relation.ispartof29th WWW: The Web Conference 2020
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleCorrecting Knowledge Base Assertionsen_US
dc.typeChapteren_US
dc.description.versionacceptedVersionen_US
dc.rights.holder© 2020 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License.en_US
dc.source.pagenumber1537-1547en_US
dc.identifier.cristin1846085
dc.relation.projectNorges forskningsråd: 237898en_US
cristin.ispublishedtrue
cristin.fulltextpostprint


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