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Global RDF Vector Space Embeddings

  • Michael Cochez
  • Petar Ristoski
  • Simone Paolo Ponzetto
  • Heiko Paulheim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10587)

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.

Keywords

Graph embeddings Linked open data Data mining 

Notes

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).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Michael Cochez
    • 1
    • 2
    • 3
  • Petar Ristoski
    • 4
  • Simone Paolo Ponzetto
    • 4
  • Heiko Paulheim
    • 4
  1. 1.Fraunhofer FITSankt AugustinGermany
  2. 2.Informatik 5RWTH University AachenAachenGermany
  3. 3.Faculty of Information TechnologyUniversity of JyvaskylaJyväskyläFinland
  4. 4.Data and Web Science GroupUniversity of MannheimMannheimGermany

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