Tensor Factorization for Multi-relational Learning

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


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.


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.


  1. 1.
    Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: Proc. of the 25th Conference on Artificial Intelligence, SF, USA (2011)Google Scholar
  2. 2.
    Jenatton, R., Le Roux, N., Bordes, A., Obozinski, G.: A latent factor model for highly multi-relational data. In: Advances in Neural Information Processing Systems, vol. 25 (2012)Google Scholar
  3. 3.
    Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Review 51(3), 455–500 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Kolda, T.G., Bader, B.W., Kenny, J.P.: Higher-order web link analysis using multilinear algebra. In: Proc. of the Fifth International Conference on Data Mining, pp. 242–249 (2005)Google Scholar
  5. 5.
    Nickel, M., Tresp, V.: Logistic tensor-factorization for multi-relational data. In: ICML Workshop - Structured Learning: Inferring Graphs from Structured and Unstructured Inputs. Atlanta, GA, USA (2013)Google Scholar
  6. 6.
    Nickel, M., Tresp, V., Kriegel, H.P.: A three-way model for collective learning on multi-relational data. In: Proc. of the 28th International Conference on Machine Learning. pp. 809—816. Bellevue, WA, USA (2011)Google Scholar
  7. 7.
    Nickel, M., Tresp, V., Kriegel, H.P.: Factorizing YAGO: scalable machine learning for linked data. In: Proc. of the 21st International World Wide Web Conference, Lyon, France (2012)Google Scholar
  8. 8.
    Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized markov chains for next-basket recommendation. In: Proc. of the 19th International Conference on World Wide Web, pp. 811–820 (2010)Google Scholar
  9. 9.
    Singla, P., Domingos, P.: Entity resolution with markov logic. In: Proc. of the Sixth International Conference on Data Mining, Washington, DC, USA, pp. 572–582 (2006)Google Scholar
  10. 10.
    Sutskever, I., Salakhutdinov, R., Tenenbaum, J.B.: Modelling relational data using bayesian clustered tensor factorization. In: Advances in Neural Information Processing Systems 22 (2009)Google Scholar
  11. 11.
    Ylmaz, Y.K., Cemgil, A.T., Simsekli, U.: Generalised coupled tensor factorisation. In: Advances in Neural Information Processing Systems (2011)Google Scholar

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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