Knowledge Graph Rule Mining via Transfer Learning

  • Pouya Ghiasnezhad OmranEmail author
  • Zhe Wang
  • Kewen Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11441)


Mining logical rules from knowledge graphs (KGs) is an important yet challenging task, especially when the relevant data is sparse. Transfer learning is an actively researched area to address the data sparsity issue, where a predictive model is learned for the target domain from that of a similar source domain. In this paper, we propose a novel method for rule learning by employing transfer learning to address the data sparsity issue, in which most relevant source KGs and candidate rules can be automatically selected for transfer. This is achieved by introducing a similarity in terms of embedding representations of entities, relations and rules. Experiments are conducted on some standard KGs. The results show that proposed method is able to learn quality rules even with extremely sparse data and its predictive accuracy outperformed state-of-the-art rule learners (AMIE+ and RLvLR), and link prediction systems (TransE and HOLE).


Knowledge graph Transfer learning Representation learning 



We would like to thank the anonymous referees for their helpful comments. This work was supported by the Australian Research Council (ARC) under DP130102302.


  1. 1.
    Bellomarini, L., Gottlob, G., Pieris, A., Sallinger, E.: Swift logic for big data and knowledge graphs. In: Proceedings of IJCAI, pp. 2–10 (2017)Google Scholar
  2. 2.
    Suchanek, F.M., Kasneci, G., Weikum, G.: YAGO: a core of semantic knowledge. In: Proceedings of WWW, pp. 697–706 (2007)Google Scholar
  3. 3.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Proceedings of ISWC, pp. 722–735 (2007)Google Scholar
  4. 4.
    Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of SIGMOD, pp. 1247–1250 (2008)Google Scholar
  5. 5.
    Pujara, J., Augustine, E., Getoor, L.: Sparsity and noise: where knowledge graph embeddings fall short. In: Proceedings of EMNLP, pp. 1751–1756 (2016)Google Scholar
  6. 6.
    Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E.: A review of relational machine learning for knowledge graphs. Proc. IEEE 104(1), 11–33 (2016)CrossRefGoogle Scholar
  7. 7.
    Barati, M., Bai, Q., Liu, Q.: SWARM: an approach for mining semantic association rules from semantic web data. In: Booth, R., Zhang, M.-L. (eds.) PRICAI 2016. LNCS (LNAI), vol. 9810, pp. 30–43. Springer, Cham (2016). Scholar
  8. 8.
    Wang, Z., Li, J.-Z.: RDF2Rules: learning rules from RDF knowledge bases by mining frequent predicate cycles. CoRR abs/1512.07734 (2015)Google Scholar
  9. 9.
    Chen, Y., Wang, D.Z., Goldberg, S.: ScaLeKB: scalable learning and inference over large knowledge bases. VLDB J. 25, 893–918 (2016)CrossRefGoogle Scholar
  10. 10.
    Galárraga, L., Teflioudi, C., Hose, K., Suchanek, F.M.: Fast rule mining in ontological knowledge bases with AMIE+. VLDB J. 24, 707–730 (2015)CrossRefGoogle Scholar
  11. 11.
    Omran, P.G., Wang, K., Wang, Z.: Scalable rule learning via learning representation. In: Proceedings of IJCAI, pp. 2149–2155 (2018)Google Scholar
  12. 12.
    Weiss, K., Khoshgoftaar, T.M., Wang, D.D.: A survey of transfer learning. J. Big Data 3, 9 (2016)CrossRefGoogle Scholar
  13. 13.
    Pan, S.J., Yang, Q.: A survey on transfer learning. TKDE 22, 1345–1359 (2010)Google Scholar
  14. 14.
    Yang, B., Yih, W., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. CoRR abs/1412.6575 (2015)Google Scholar
  15. 15.
    Neelakantan, A., Roth, B., McCallum, A.: Compositional vector space models for knowledge base inference. In: Proceedings of AAAI Spring Symposia (2015)Google Scholar
  16. 16.
    Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Proceedings of NIPS, pp. 2787–2795 (2013)Google Scholar
  17. 17.
    Nickel, M., Rosasco, L., Poggio, T.: Holographic embeddings of knowledge graphs. In: Proceedings of AAAI, pp. 1955–1961 (2016)Google Scholar
  18. 18.
    Nickel, M., Tresp, V., Kriegel, H.-P.: A three-way model for collective learning on multi-relational data. In: Proceedings of ICML, pp. 809–816 (2011)Google Scholar
  19. 19.
    Zhu, H., Xie, R., Liu, Z., Sun, M.: Iterative entity alignment via knowledge embeddings. In: Proceedings of IJCAI, pp. 4258–4264 (2017)Google Scholar
  20. 20.
    Yang, F., Yang, Z., Cohen, W.W.: Differentiable learning of logical rules for knowledge base reasoning. In: Proceedings of NIPS, pp. 2316–2325 (2017)Google Scholar
  21. 21.
    Van Haaren, J., Kolobov, A., Davis, J.: TODTLER: two-order-deep transfer learning. In: Proceedings of AAAI, pp. 3007–3015 (2015)Google Scholar
  22. 22.
    Omran, P.G., Wang, K., Wang, Z.: Transfer learning in probabilistic logic models. In: Kang, B.H., Bai, Q. (eds.) AI 2016. LNCS (LNAI), vol. 9992, pp. 378–389. Springer, Cham (2016). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Pouya Ghiasnezhad Omran
    • 1
    Email author
  • Zhe Wang
    • 1
    • 2
  • Kewen Wang
    • 1
  1. 1.Griffith UniversityBrisbaneAustralia
  2. 2.State Key Laboratory of Computer Science, Institute of SoftwareChinese Academy of SciencesBeijingChina

Personalised recommendations