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dc.contributor.authorCochez, Michael
dc.contributor.authorRistoski, Petar
dc.contributor.authorPonzetto, Simone Paolo
dc.contributor.authorPaulheim, Heiko
dc.contributor.editord'Amato, Claudia
dc.contributor.editorFernandez, Miriam
dc.contributor.editorTamma, Valentina
dc.contributor.editorLecue, Freddy
dc.contributor.editorCudré-Mauroux, Philippe
dc.contributor.editorSequeda, Juan
dc.contributor.editorLange, Christoph
dc.contributor.editorHeflin, Jeff
dc.date.accessioned2017-12-13T11:03:16Z
dc.date.available2017-12-13T11:03:16Z
dc.date.issued2017
dc.identifier.citationCochez, M., Ristoski, P., Ponzetto, S. P., & Paulheim, H. (2017). Global RDF Vector Space Embeddings. In C. d'Amato, M. Fernandez, V. Tamma, F. Lecue, P. Cudré-Mauroux, J. Sequeda, C. Lange, & J. Heflin (Eds.), <i>ISWC 2017 - The Semantic Web : 16th International Semantic Web Conference, Proceedings, Part I</i> (pp. 190-207). Springer. Lecture Notes in Computer Science, 10587. <a href="https://doi.org/10.1007/978-3-319-68288-4_12" target="_blank">https://doi.org/10.1007/978-3-319-68288-4_12</a>
dc.identifier.otherCONVID_27351483
dc.identifier.otherTUTKAID_75690
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/56299
dc.description.abstractVector space embeddings have been shown to perform well when using RDF data in data mining and machine learning tasks. Existing approaches, such as RDF2Vec, use local information, i.e., they rely on local sequences generated for nodes in the RDF graph. For word embeddings, global techniques, such as GloVe, have been proposed as an alternative. In this paper, we show how the idea of global embeddings can be transferred to RDF embeddings, and show that the results are competitive with traditional local techniques like RDF2Vec.
dc.format.extent764
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofISWC 2017 - The Semantic Web : 16th International Semantic Web Conference, Proceedings, Part I
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.subject.othergraph embeddings
dc.titleGlobal RDF Vector Space Embeddings
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201712114610
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2017-12-11T13:15:22Z
dc.relation.isbn978-3-319-68287-7
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange190-207
dc.relation.issn0302-9743
dc.type.versionacceptedVersion
dc.rights.copyright© Springer International Publishing AG 2017. This is a final draft version of an article whose final and definitive form has been published by Springer. Published in this repository with the kind permission of the publisher.
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational Semantic Web Conference
dc.subject.ysotiedonlouhinta
dc.subject.ysosemanttinen web
dc.subject.ysoyhdistetty avoin tieto
jyx.subject.urihttp://www.yso.fi/onto/yso/p5520
jyx.subject.urihttp://www.yso.fi/onto/yso/p21716
jyx.subject.urihttp://www.yso.fi/onto/yso/p26001
dc.relation.doi10.1007/978-3-319-68288-4_12
dc.type.okmA4


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