Global RDF vector space embeddings

Michael Cochez; Petar Ristoski; Simone Paolo Ponzetto; Heiko Paulheim

doi:10.1007/978-3-319-68288-4_12

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 proceeding › Conference contribution › Academic › peer-review

Abstract

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 language	English
Title of host publication	The Semantic Web – ISWC 2017 - 16th International Semantic Web Conference, Proceedings
Editors	Philippe Cudre-Mauroux, Christoph Lange, Claudia d’Amato, Miriam Fernandez, Jeff Heflin, Freddy Lecue, Valentina Tamma, Juan Sequeda
Publisher	Springer Verlag
Pages	190-207
Number of pages	18
ISBN (Print)	9783319682877
DOIs	https://doi.org/10.1007/978-3-319-68288-4_12
Publication status	Published - 1 Jan 2017
Externally published	Yes
Event	16th International Semantic Web Conference, ISWC 2017 - Vienna, Austria Duration: 21 Oct 2017 → 25 Oct 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10587 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Semantic Web Conference, ISWC 2017
Country/Territory	Austria
City	Vienna
Period	21/10/17 → 25/10/17

Funding

Acknowledgements. The work presented in this paper has been partially funded by the Junior-professor funding programme of the Ministry of Science, Research and the Arts of the state of Baden-Württemberg (project “Deep semantic models for high-end NLP application”), and by the German Research Foundation (DFG) under grant number PA 2373/1-1 (Mine@LOD).

Funders	Funder number
Ministry of Science, Research and the Arts of the state of Baden-Württemberg
Deutsche Forschungsgemeinschaft	PA 2373/1-1

Keywords

Data mining
Graph embeddings
Linked open data

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10.1007/978-3-319-68288-4_12

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Cochez, M., Ristoski, P., Ponzetto, S. P., & Paulheim, H. (2017). Global RDF vector space embeddings. In P. Cudre-Mauroux, C. Lange, C. d’Amato, M. Fernandez, J. Heflin, F. Lecue, V. Tamma, & J. Sequeda (Eds.), The Semantic Web – ISWC 2017 - 16th International Semantic Web Conference, Proceedings (pp. 190-207). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10587 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-68288-4_12

Cochez, Michael ; Ristoski, Petar ; Ponzetto, Simone Paolo et al. / Global RDF vector space embeddings. The Semantic Web – ISWC 2017 - 16th International Semantic Web Conference, Proceedings. editor / Philippe Cudre-Mauroux ; Christoph Lange ; Claudia d’Amato ; Miriam Fernandez ; Jeff Heflin ; Freddy Lecue ; Valentina Tamma ; Juan Sequeda. Springer Verlag, 2017. pp. 190-207 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Cochez, M, Ristoski, P, Ponzetto, SP & Paulheim, H 2017, Global RDF vector space embeddings. in P Cudre-Mauroux, C Lange, C d’Amato, M Fernandez, J Heflin, F Lecue, V Tamma & J Sequeda (eds), The Semantic Web – ISWC 2017 - 16th International Semantic Web Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10587 LNCS, Springer Verlag, pp. 190-207, 16th International Semantic Web Conference, ISWC 2017, Vienna, Austria, 21/10/17. https://doi.org/10.1007/978-3-319-68288-4_12

Global RDF vector space embeddings. / Cochez, Michael; Ristoski, Petar; Ponzetto, Simone Paolo et al.
The Semantic Web – ISWC 2017 - 16th International Semantic Web Conference, Proceedings. ed. / Philippe Cudre-Mauroux; Christoph Lange; Claudia d’Amato; Miriam Fernandez; Jeff Heflin; Freddy Lecue; Valentina Tamma; Juan Sequeda. Springer Verlag, 2017. p. 190-207 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10587 LNCS).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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T1 - Global RDF vector space embeddings

AU - Cochez, Michael

AU - Ristoski, Petar

AU - Ponzetto, Simone Paolo

AU - Paulheim, Heiko

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Y1 - 2017/1/1

N2 - 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.

AB - 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.

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KW - Graph embeddings

KW - Linked open data

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A2 - Heflin, Jeff

A2 - Lecue, Freddy

A2 - Tamma, Valentina

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PB - Springer Verlag

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ER -

Cochez M, Ristoski P, Ponzetto SP, Paulheim H. Global RDF vector space embeddings. In Cudre-Mauroux P, Lange C, d’Amato C, Fernandez M, Heflin J, Lecue F, Tamma V, Sequeda J, editors, The Semantic Web – ISWC 2017 - 16th International Semantic Web Conference, Proceedings. Springer Verlag. 2017. p. 190-207. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-68288-4_12

Global RDF vector space embeddings

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