Link Prediction in Knowledge Graphs with Concepts of Nearest Neighbours

  • Sébastien FerréEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11503)


The open nature of Knowledge Graphs (KG) often implies that they are incomplete. Link prediction consists in inferring new links between the entities of a KG based on existing links. Most existing approaches rely on the learning of latent feature vectors for the encoding of entities and relations. In general however, latent features cannot be easily interpreted. Rule-based approaches offer interpretability but a distinct ruleset must be learned for each relation, and computation time is difficult to control. We propose a new approach that does not need a training phase, and that can provide interpretable explanations for each inference. It relies on the computation of Concepts of Nearest Neighbours (CNN) to identify similar entities based on common graph patterns. Dempster-Shafer theory is then used to draw inferences from CNNs. We evaluate our approach on FB15k-237, a challenging benchmark for link prediction, where it gets competitive performance compared to existing approaches.



I warmly thank Luis Galárraga for his support about AMIE+.


  1. 1.
    Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Sci. Am. 284(5), 34–43 (2001)CrossRefGoogle 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.
    Denœux, T.: A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Trans. Syst. Man Cybern. 25(5), 804–813 (1995)CrossRefGoogle Scholar
  4. 4.
    Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D knowledge graph embeddings. In: McIlraith, S.A., Weinberger, K.Q. (eds.) Conference on Artificial Intelligence (AAAI), pp. 1811–1818. AAAI Press (2018)Google Scholar
  5. 5.
    Ferré, S.: Concepts de plus proches voisins dans des graphes de connaissances. In: Ingénierie des Connaissances (IC), pp. 163–174 (2017)Google Scholar
  6. 6.
    Ferré, S.: Answers partitioning and lazy joins for efficient query relaxation and application to similarity search. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 209–224. Springer, Cham (2018). Scholar
  7. 7.
    Ferré, S., Cellier, P.: Graph-FCA in practice. In: Haemmerlé, O., Stapleton, G., Faron Zucker, C. (eds.) ICCS 2016. LNCS (LNAI), vol. 9717, pp. 107–121. Springer, Cham (2016). Scholar
  8. 8.
    Galárraga, L., Teflioudi, C., Hose, K., Suchanek, F.: Fast rule mining in ontological knowledge bases with AMIE+. Int. J. Very Large Data Bases 24(6), 707–730 (2015)CrossRefGoogle Scholar
  9. 9.
    Hermann, A., Ferré, S., Ducassé, M.: An interactive guidance process supporting consistent updates of RDFS graphs. In: ten Teije, A., et al. (eds.) EKAW 2012. LNCS (LNAI), vol. 7603, pp. 185–199. Springer, Heidelberg (2012). Scholar
  10. 10.
    Jianfeng, W., Jianxin, L., Yongyi, M., Shini, C., Richong, Z.: On the representation and embedding of knowledge bases beyond binary relations. In: International Joint Conference on Artificial Intelligence (IJCAI), pp. 1300–1307 (2016)Google Scholar
  11. 11.
    Lao, N., Mitchell, T., Cohen, W.W.: Random walk inference and learning in a large scale knowledge base. In: Conference on Empirical Methods in Natural Language Processing, pp. 529–539. Association for Computational Linguistics (2011)Google Scholar
  12. 12.
    Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inform. Sci. Technol. 58(7), 1019–1031 (2007)CrossRefGoogle Scholar
  13. 13.
    May, W.: Information extraction and integration with Florid: the Mondial case study. Technical report 131, Universität Freiburg, Institut für Informatik (1999).
  14. 14.
    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). Scholar
  15. 15.
    Muggleton, S.: Inverse entailment and Progol. New Gener. Comput. 13, 245–286 (1995)CrossRefGoogle Scholar
  16. 16.
    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
  17. 17.
    Plotkin, G.: Automatic methods of inductive inference. Ph.D. thesis, Edinburgh University, August 1971Google Scholar
  18. 18.
    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). Scholar
  19. 19.
    Toutanova, K., Chen, D.: Observed versus latent features for knowledge base and text inference. In: Workshop on Continuous Vector Space Models and Their Compositionality, pp. 57–66 (2015)Google Scholar
  20. 20.
    Zhang, R., Li, J., Mei, J., Mao, Y.: Scalable instance reconstruction in knowledge bases via relatedness affiliated embedding. In: Conference on World Wide Web (WWW), pp. 1185–1194 (2018)Google Scholar

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Authors and Affiliations

  1. 1.Univ Rennes, CNRS, IRISARennesFrance

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