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dc.contributor.authorZhao, L
dc.contributor.authorZhang, X
dc.contributor.authorWang, K
dc.contributor.authorFeng, Z
dc.contributor.authorWang, Z
dc.date.accessioned2020-03-23T03:41:45Z
dc.date.available2020-03-23T03:41:45Z
dc.date.issued2019
dc.identifier.issn1613-0073
dc.identifier.urihttp://hdl.handle.net/10072/392548
dc.description.abstractThis presents a representation learning model called SetE by modeling a predicate into a subspace in a semantic space where entities are vectors. Within SetE, a type as unary predicate is encoded as a set of vectors and a relation as binary predicate is encoded as a set of pairs of vectors. A new approach is proposed to compute the subsumption of predicates in a semantic space by employing linear programming methods to determine whether entities of a type belong to a sup-type and thus an algorithm for learning OWL axioms is developed. Experiments on real datasets show that SetE can efficiently learn various forms of axioms with high quality.
dc.description.peerreviewedYes
dc.publisherRheinisch-Westfaelische Technische Hochschule Aachen
dc.publisher.urihttp://ceur-ws.org/Vol-2456/
dc.relation.ispartofconferencename18th International Semantic Web Conference (ISWC 2019)
dc.relation.ispartofconferencetitleCEUR Workshop Proceedings
dc.relation.ispartofdatefrom2019-10-26
dc.relation.ispartofdateto2019-10-30
dc.relation.ispartoflocationAuckland, New Zealand
dc.relation.ispartofpagefrom57
dc.relation.ispartofpageto60
dc.relation.ispartofvolume2456
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode0801
dc.titleLearning ontology axioms over knowledge graphs via representation learning
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationZhao, L; Zhang, X; Wang, K; Feng, Z; Wang, Z, Learning ontology axioms over knowledge graphs via representation learning, CEUR Workshop Proceedings, 2019, 2456, pp. 57-60
dcterms.licensehttps://creativecommons.org/licenses/by/4.0/
dc.date.updated2020-03-23T03:39:26Z
dc.description.versionPublished
gro.rights.copyright© The Author(s) 2019. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorWang, Zhe
gro.griffith.authorWang, Kewen


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