Global RDF vector space embeddings

Michael Cochez*, Petar Ristoski, Simone Paolo Ponzetto, Heiko Paulheim

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review


Vector 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.

Original languageEnglish
Title of host publicationThe Semantic Web – ISWC 2017 - 16th International Semantic Web Conference, Proceedings
EditorsPhilippe Cudre-Mauroux, Christoph Lange, Claudia d’Amato, Miriam Fernandez, Jeff Heflin, Freddy Lecue, Valentina Tamma, Juan Sequeda
PublisherSpringer Verlag
Number of pages18
ISBN (Print)9783319682877
Publication statusPublished - 1 Jan 2017
Externally publishedYes
Event16th International Semantic Web Conference, ISWC 2017 - Vienna, Austria
Duration: 21 Oct 201725 Oct 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10587 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Semantic Web Conference, ISWC 2017


  • Data mining
  • Graph embeddings
  • Linked open data


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