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Tensor Factorization for Multi-relational Learning

  • Maximilian Nickel
  • Volker Tresp
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8190)

Abstract

Tensor factorization has emerged as a promising approach for solving relational learning tasks. Here we review recent results on a particular tensor factorization approach, i.e. Rescal, which has demonstrated state-of-the-art relational learning results, while scaling to knowledge bases with millions of entities and billions of known facts.

Keywords

Link Prediction Relational Learning Entity Resolution Party Membership Latent Factor Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Maximilian Nickel
    • 1
  • Volker Tresp
    • 2
  1. 1.Ludwig Maximilian UniversityMunichGermany
  2. 2.Siemens AG, Corporate TechnologyMunichGermany

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