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dc.contributor.authorWu, H
dc.contributor.authorWang, Z
dc.contributor.authorZhang, X
dc.contributor.authorOmran, PG
dc.contributor.authorFeng, Z
dc.contributor.authorWang, K
dc.date.accessioned2020-03-23T03:38:37Z
dc.date.available2020-03-23T03:38:37Z
dc.date.issued2019
dc.identifier.issn1613-0073
dc.identifier.urihttp://hdl.handle.net/10072/392547
dc.description.abstractThis poster paper presents an efficient method R-Linker for link prediction in large knowledge graphs, based on rule learning. The scalability and efficiency is achieved by a combination of several optimisation techniques. Experimental results show that R-Linker is able to handle KGs with over 10 million of entities and more efficient than existing state-of-the-art methods including RLvLR and AMIE+ in rule learning stage for link prediction.
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.ispartofpagefrom121
dc.relation.ispartofpageto124
dc.relation.ispartofvolume2456
dc.subject.fieldofresearchComputation Theory and Mathematics
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode0802
dc.subject.fieldofresearchcode0801
dc.titleA system for reasoning-based link prediction in large knowledge graphs
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationWu, H; Wang, Z; Zhang, X; Omran, PG; Feng, Z; Wang, K, A system for reasoning-based link prediction in large knowledge graphs, CEUR Workshop Proceedings, 2019, 2456, pp. 121-124
dcterms.licensehttps://creativecommons.org/licenses/by/4.0/
dc.date.updated2020-03-23T03:34:48Z
dc.description.versionVersion of Record (VoR)
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
gro.griffith.authorWu, Hong


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