Advertisement

A Preference-Learning Framework for Modeling Relational Data

  • Ivano LauriolaEmail author
  • Mirko Polato
  • Guglielmo Faggioli
  • Fabio Aiolli
Conference paper
Part of the Proceedings of the International Neural Networks Society book series (INNS, volume 1)

Abstract

Nowadays large scale Knowledge Bases (KBs) represent very important resources when it comes to develop expert systems. However, despite their huge sizes, KBs often suffer from incompleteness. Recently, much effort has been devoted in developing learning models to reduce the aforementioned issue.

In this work, we show how relational learning tasks, such as link prediction, can be cast into a preference learning tasks. In particular, we propose a preference learning method, called REC-PLM, for learning low-dimensional representations of entities and relations in a KB. Being highly parallelizable, REC-PLM is a powerful resource to deal with high-dimensional modern KBs. Experiments against state-of-the-art methods on a large scale KB show the potential of the proposed approach.

Keywords

Preference learning Embeddings Knowledge base Relational data Relational learning 

References

  1. 1.
    Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, SIGMOD 2008, pp. 1247–1250 (2008)Google Scholar
  2. 2.
    Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., Bizer, C.: DBpedia - a large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web J. 6(2), 167–195 (2015)Google Scholar
  3. 3.
    Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  4. 4.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2, NIPS 2013, pp. 3111–3119 (2013)Google Scholar
  5. 5.
    Bordes, A., Usunier, N., Garcia-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2, NIPS 2013, pp. 2787–2795 (2013)Google Scholar
  6. 6.
    Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, AAAI 2014, pp. 1112–1119 (2014)Google Scholar
  7. 7.
    Fan, M., Zhou, Q., Chang, E., Zheng, T.F.: Transition-based knowledge graph embedding with relational mapping properties. In: PACLIC (2014)Google Scholar
  8. 8.
    Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI 2015, pp. 2181–2187 (2015)Google Scholar
  9. 9.
    Nguyen, D.Q., Sirts, K., Qu, L., Johnson, M.: STransE: a novel embedding model of entities and relationships in knowledge bases. In: HLT-NAACL (2016)Google Scholar
  10. 10.
    Nickel, M., Tresp, V., Kriegel, H.-P.: A three-way model for collective learning on multi-relational data. In: Proceedings of the 28th International Conference on International Conference on Machine Learning, ICML 2011, pp. 809–816 (2011)Google Scholar
  11. 11.
    Bordes, A., Glorot, X., Weston, J., Bengio, Y.: A semantic matching energy function for learning with multi-relational data. Mach. Learn. 94(2), 233–259 (2014)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Socher, R., Chen, D., Manning, C.D., Ng, A.Y.: Reasoning with neural tensor networks for knowledge base completion. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 1, NIPS 2013, pp. 926–934 (2013)Google Scholar
  13. 13.
    Lauriola, I., Polato, M., Lavelli, A., Rinaldi, F., Aiolli, F.: Learning preferences for large scale multi-label problems. In: International Conference on Artificial Neural Networks, pp. 546–555. Springer (2018)Google Scholar
  14. 14.
    Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2011, pp. 301–306 (2011)Google Scholar
  15. 15.
    Yang, B., Yih, W., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of the International Conference on Learning Representations (2015)Google Scholar
  16. 16.
    Nickel, M., Rosasco, L., Poggio, T.: Holographic embeddings of knowledge graphs. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI 2016, pp. 1955–1961 (2016)Google Scholar
  17. 17.
    Freund, Y., Schapire, R.E.: Large margin classification using the perceptron algorithm. Mach. Learn. 37(3), 277–296 (1999)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ivano Lauriola
    • 1
    • 2
    Email author
  • Mirko Polato
    • 1
  • Guglielmo Faggioli
    • 1
  • Fabio Aiolli
    • 1
  1. 1.Department of MathematicsUniversity of PadovaPaduaItaly
  2. 2.Bruno Kessler FoundationTrentoItaly

Personalised recommendations