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dc.contributor.authorGhiasnezhad Omran, Pouya
dc.contributor.authorWang, K
dc.contributor.authorWang, Z
dc.date.accessioned2020-03-23T03:45:49Z
dc.date.available2020-03-23T03:45:49Z
dc.date.issued2019
dc.identifier.issn1613-0073
dc.identifier.urihttp://hdl.handle.net/10072/392549
dc.description.abstractKnowledge Graphs (KGs) are a prevailing data management approach and have found extensive applications in recent years. While several methods have been proposed for learning schema information for KGs in the form of logical rules, they are not suitable for KGs with constantly evolving data. This paper makes the first attempt to address the problem by presenting an approach to learning temporal rules from KG streams. The learned temporal rules can be applied in link prediction and event prediction over KG streams. Based on the proposed method, a system StreamLearner has been implemented. Our experimental results show that StreamLearner is effective and efficient in learning temporal rules on real-life datasets and significantly outperforms some state-of-the-art systems that do not account for temporal knowledge or evolving data.
dc.description.peerreviewedYes
dc.publisherAAAI
dc.publisher.urihttp://ceur-ws.org/Vol-2350/
dc.relation.ispartofconferencenameAAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE)
dc.relation.ispartofconferencetitleCEUR Workshop Proceedings
dc.relation.ispartofdatefrom2019-03-25
dc.relation.ispartofdateto2019-03-27
dc.relation.ispartoflocationPalo Alto, USA
dc.relation.ispartofpagefrom1
dc.relation.ispartofpagefrom8 pages
dc.relation.ispartofpageto8
dc.relation.ispartofpageto8 pages
dc.relation.ispartofvolume2350
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode0801
dc.titleLearning temporal rules from knowledge graph streams
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationGhiasnezhad Omran, P; Wang, K; Wang, Z, Learning temporal rules from knowledge graph streams, CEUR Workshop Proceedings, 2019, 2350, pp. 1-8
dcterms.licensehttps://creativecommons.org/licenses/by/4.0/
dc.date.updated2020-03-23T03:43:11Z
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, Kewen
gro.griffith.authorWang, Zhe
gro.griffith.authorGhiasnezhad Omran, Pouya


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