Advertisement

An Introduction to AnyBURL

  • Christian MeilickeEmail author
  • Melisachew Wudage Chekol
  • Daniel Ruffinelli
  • Heiner Stuckenschmidt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11793)

Abstract

Current research on knowledge graph completion is often concerned with latent approaches that are based on the idea to embed a knowledge graph into a low dimensional vector space. At the same time symbolic approaches have attracted less attention [13]. However, such approaches have a big advantage: they yield an explanation in terms of the rules that trigger a prediction. In this paper we propose a bottom-up technique for efficiently learning logical rules from large knowledge graphs inspired by classic bottom-up rule learning approaches as Golem [8] and Aleph [10]. Our approach is called AnyBURL (Anytime Bottom-Up Rule Learning). We report on experiments where we evaluated AnyBURL on datasets that have been labelled as hard cases for simple (rule-based) approaches. Our approach performs as good as and sometimes better than most models that have been proposed recently. Moreover, the required resources in terms of memory and runtime are significantly smaller compared to latent approaches. This paper is an extended abstract of an IJCAI 2019 paper [6].

References

  1. 1.
    Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, pp. 2787–2795 (2013)Google Scholar
  2. 2.
    Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D knowledge graph embeddings. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)Google Scholar
  3. 3.
    Galárraga, L., Teflioudi, C., Hose, K., Suchanek, F.M.: Fast rule mining in ontological knowledge bases with AMIE+. VLDB J.- Int. J. Very Large Data Bases 24(6), 707–730 (2015)CrossRefGoogle Scholar
  4. 4.
    Kazemi, S.M., Poole, D.: Simple embedding for link prediction in knowledge graphs. In: Advances in Neural Information Processing Systems, pp. 4289–4300 (2018)Google Scholar
  5. 5.
    Lacroix, T., Usunier, N., Obozinski, G.: Canonical tensor decomposition for knowledge base completion. In: ICML, pp. 2869–2878 (2018)Google Scholar
  6. 6.
    Meilicke, C., Chekol, M.W., Ruffinelli, D., Stuckenschmidt, H.: Anytime bottom-up rule learning for knowledge graph completion. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI 2019) (2019)Google Scholar
  7. 7.
    Meilicke, C., Fink, M., Wang, Y., Ruffinelli, D., Gemulla, R., Stuckenschmidt, H.: Fine-grained evaluation of rule- and embedding-based systems for knowledge graph completion. In: Vrandečić, D., et al. (eds.) ISWC 2018. LNCS, vol. 11136, pp. 3–20. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00671-6_1CrossRefGoogle Scholar
  8. 8.
    Muggleton, S.H., Feng, C.: Efficient induction of logic programs. In: Proceedings of the First Conference on Algorithmic Learning Theory, pp. 368–381 (1990)Google Scholar
  9. 9.
    Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593–607. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-93417-4_38CrossRefGoogle Scholar
  10. 10.
    Srinivasan, A.: The aleph manual. Technical report, Computing Laboratory, Oxford University (2000)Google Scholar
  11. 11.
    Toutanova, K., Chen, D.: Observed versus latent features for knowledge base and text inference. In: Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality, pp. 57–66 (2015)Google Scholar
  12. 12.
    Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. In: International Conference on Machine Learning, pp. 2071–2080 (2016)Google Scholar
  13. 13.
    Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724–2743 (2017)CrossRefGoogle Scholar
  14. 14.
    Zhang, W., Paudel, B., Zhang, W., Bernstein, A., Chen, H.: Interaction embeddings for prediction and explanation in knowledge graphs. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 96–104. ACM (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Christian Meilicke
    • 1
    Email author
  • Melisachew Wudage Chekol
    • 1
  • Daniel Ruffinelli
    • 1
  • Heiner Stuckenschmidt
    • 1
  1. 1.University of MannheimMannheimGermany

Personalised recommendations