Show simple item record

dc.contributor.authorGhiasnezhad Omran, P
dc.contributor.authorWang, K
dc.contributor.authorWang, Z
dc.date.accessioned2019-10-02T22:47:00Z
dc.date.available2019-10-02T22:47:00Z
dc.date.issued2019
dc.identifier.issn1041-4347
dc.identifier.doi10.1109/TKDE.2019.2941685
dc.identifier.urihttp://hdl.handle.net/10072/387994
dc.description.abstractIt is natural and effective to use rules for representing explicit knowledge in knowledge graphs. However, it is challenging to learn rules automatically from very large knowledge graphs such as Freebase and YAGO. This paper presents a new approach, RLvLR (Rule Learning via Learning Representations), to learning rules from large knowledge graphs by using the technique of embedding in representation learning together with a new sampling method. Based on RLvLR, a new method RLvLR-Stream is developed for learning rules from streams of knowledge graphs. Both RLvLR and RLvLR-Stream have been implemented and experiments conducted to validate the proposed methods regarding the tasks of rule learning and link prediction. Experimental results show that our systems are able to handle the task of rule learning from large knowledge graphs with high accuracy and outperform some state-of-the-art systems. Specifically, for massive knowledge graphs with hundreds of predicates and over 10M facts, RLvLR is much faster and can learn much more quality rules than major systems for rule learning in knowledge graphs such as AMIE+. In the setting of knowledge graph streams, RLvLR-Stream significantly improved RLvLR for both rule learning and link prediction.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers
dc.publisher.placeUnited States
dc.relation.ispartofjournalIEEE Transactions on Knowledge and Data Engineering
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchcode08
dc.titleAn Embedding-based Approach to Rule Learning in Knowledge Graphs
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationGhiasnezhad Omran, P; Wang, K; Wang, Z, An Embedding-based Approach to Rule Learning in Knowledge Graphs, IEEE Transactions on Knowledge and Data Engineering, 2019
dc.date.updated2019-10-01T23:27:21Z
dc.description.versionAccepted Manuscript (AM)
gro.description.notepublicThis publication has been entered into Griffith Research Online as an Advanced Online Version.
gro.rights.copyright© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.hasfulltextFull Text
gro.griffith.authorWang, Zhe
gro.griffith.authorWang, Kewen
gro.griffith.authorGhiasnezhad Omran, Pouya


Files in this item

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record