PDKE: an efficient distributed embedding framework for large knowledge graphs Dong, Sicong, Wang, Xin, Chai, Lele, Li, Jianxin and Yang, Yajun 2020, PDKE: an efficient distributed embedding framework for large knowledge graphs, in DASFAA 2020 : Proceedings of the 25th International Conference on Database Systems for Advanced Applications, Springer, Cham, Switzerland, pp. 588-603, doi: 10.1007/978-3-030-59416-9_35. Attached Files Name Description MIMEType Size Downloads Title PDKE: an efficient distributed embedding framework for large knowledge graphs Author(s) Dong, Sicong Wang, Xin Chai, Lele Li, Jianxin orcid.org/0000-0002-9059-330X Yang, Yajun Conference name DASFAA Database Systems for Advanced Applications. International Conference (25th : 2020 : Jeju, South Korea) Conference location Jeju, South Korea Conference dates 24 - 27 Sep. 2020 Title of proceedings DASFAA 2020 : Proceedings of the 25th International Conference on Database Systems for Advanced Applications Editor(s) Nah, Yunmook Cui, Bin Lee, Sang-Won Yu, Jeffrey Xu Moon, Yang-Sae Whang, Steven Euijong Publication date 2020 Series Lecture Notes in Computer Science Start page 588 End page 603 Total pages 16 Publisher Springer Place of publication Cham, Switzerland Keyword(s) knowledge representation learning knowledge graphs distributed framework knowledge embedding CORE2020 B ISBN 9783030594152 ISSN 0302-9743 1611-3349 Language eng DOI 10.1007/978-3-030-59416-9_35 Indigenous content off HERDC Research category E1 Full written paper - refereed Copyright notice ©2020, Springer Nature Switzerland Persistent URL http://hdl.handle.net/10536/DRO/DU:30144306 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 19 Oct 2020, 07:12:04 EST Mon, 19 Oct 2020, 10:33:40 EST Tue, 20 Oct 2020, 04:00:39 EST Tue, 20 Oct 2020, 05:06:10 EST Wed, 21 Oct 2020, 06:35:03 EST Wed, 21 Oct 2020, 06:40:22 EST Wed, 21 Oct 2020, 07:02:15 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 37 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Mon, 19 Oct 2020, 10:33:40 EST

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