Advertisement

Fine-Grained Evaluation of Rule- and Embedding-Based Systems for Knowledge Graph Completion

  • Christian MeilickeEmail author
  • Manuel Fink
  • Yanjie Wang
  • Daniel Ruffinelli
  • Rainer Gemulla
  • Heiner Stuckenschmidt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11136)

Abstract

Over the recent years, embedding methods have attracted increasing focus as a means for knowledge graph completion. Similarly, rule-based systems have been studied for this task in the past. What is missing so far is a common evaluation that includes more than one type of method. We close this gap by comparing representatives of both types of systems in a frequently used evaluation protocol. Leveraging the explanatory qualities of rule-based systems, we present a fine-grained evaluation that gives insight into characteristics of the most popular datasets and points out the different strengths and shortcomings of the examined approaches. Our results show that models such as TransE, RESCAL or HolE have problems in solving certain types of completion tasks that can be solved by a rule-based approach with high precision. At the same time, there are other completion tasks that are difficult for rule-based systems. Motivated by these insights, we combine both families of approaches via ensemble learning. The results support our assumption that the two methods complement each other in a beneficial way.

References

  1. 1.
    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
  2. 2.
    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
  3. 3.
    Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D knowledge graph embeddings. CoRR abs/1707.01476 (2017). http://arxiv.org/abs/1707.01476
  4. 4.
    Galárraga, L.A., Teflioudi, C., Hose, K., Suchanek, F.: AMIE: association rule mining under incomplete evidence in ontological knowledge bases. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 413–422. ACM (2013)Google Scholar
  5. 5.
    Gardner, M., Mitchell, T.M.: Efficient and expressive knowledge base completion using subgraph feature extraction. In: EMNLP, pp. 1488–1498 (2015)Google Scholar
  6. 6.
    Kadlec, R., Bajgar, O., Kleindienst, J.: Knowledge base completion: baselines strike back. arXiv preprint arXiv:1705.10744 (2017)
  7. 7.
    Lao, N., Mitchell, T., Cohen, W.W.: Random walk inference and learning in a large scale knowledge base. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 529–539. Association for Computational Linguistics (2011)Google Scholar
  8. 8.
    Nickel, M., Rosasco, L., Poggio, T.A., et al.: Holographic embeddings of knowledge graphs. In: AAAI, pp. 1955–1961 (2016)Google Scholar
  9. 9.
    Nickel, M., Tresp, V., Kriegel, H.P.: A three-way model for collective learning on multi-relational data. In: ICML, vol. 11, pp. 809–816 (2011)Google Scholar
  10. 10.
    Niepert, M.: Discriminative Gaifman models. In: Advances in Neural Information Processing Systems, pp. 3405–3413 (2016)Google Scholar
  11. 11.
    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
  12. 12.
    Shen, Y., Huang, P.S., Chang, M.W., Gao, J.: Traversing knowledge graph in vector space without symbolic space guidance. arXiv preprint arXiv:1611.04642 (2016)
  13. 13.
    Shi, B., Weninger, T.: ProjE: embedding projection for knowledge graph completion. In: AAAI, vol. 17, pp. 1236–1242 (2017)Google Scholar
  14. 14.
    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
  15. 15.
    Wang, Y., Gemulla, R., Li, H.: On multi-relational link prediction with bilinear models. In: Association for the Advancement of Artificial Intelligence, AAAI (2018). https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16900
  16. 16.
    Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: AAAI, vol. 14, pp. 1112–1119 (2014)Google Scholar
  17. 17.
    Xiao, H., Huang, M., Zhu, X.: TransG: a generative model for knowledge graph embedding. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 2316–2325 (2016)Google Scholar
  18. 18.
    Yang, B., Yih, W., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575 (2014)
  19. 19.
    Zeng, Q., Patel, J.M., Page, D.: QuickFOIL: scalable inductive logic programming. Proc. VLDB Endow. 8(3), 197–208 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Christian Meilicke
    • 1
    Email author
  • Manuel Fink
    • 1
  • Yanjie Wang
    • 1
  • Daniel Ruffinelli
    • 1
  • Rainer Gemulla
    • 1
  • Heiner Stuckenschmidt
    • 1
  1. 1.Research Group Data and Web ScienceUniversity of MannheimMannheimGermany

Personalised recommendations