EMT: A Tail-Oriented Method for Specific Domain Knowledge Graph Completion

  • Yi Zhang
  • Zhijuan Du
  • Xiaofeng MengEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11441)


The basic unit of knowledge graph is triplet, including head entity, relation and tail entity. Centering on knowledge graph, knowledge graph completion has attracted more and more attention and made great progress. However, these models are all verified by open domain data sets. When applied in specific domain case, they will be challenged by practical data distributions. For example, due to poor presentation of tail entities caused by their relation-oriented feature, they can not deal with the completion of enzyme knowledge graph. Inspired by question answering and rectilinear propagation of lights, this paper puts forward a tail-oriented method - Embedding for Multi-Tails knowledge graph (EMT). Specifically, it first represents head and relation in question space; then, finishes projection to answer one by tail-related matrix; finally, gets tail entity via translating operation in answer space. To overcome time-space complexity of EMT, this paper includes two improved models: EMT\(^v\) and EMT\(^s\). Taking some optimal translation and composition models as baselines, link prediction and triplets classification on an enzyme knowledge graph sample and Kinship proved our performance improvements, especially in tails prediction.


Knowledge graph Knowledge graph completion Specific domain knowledge graph Embedding Tail-oriented 


  1. 1.
    Belleau, F., Nolin, M.A., Tourigny, N., Rigault, P., Morissette, J.: Bio2RDF: towards a mashup to build bioinformatics knowledge systems. J. Biomed. Inform. 41(5), 706–716 (2008)CrossRefGoogle Scholar
  2. 2.
    Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: SIGMOD, pp. 1247–1250. ACM (2008)Google Scholar
  3. 3.
    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)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: NIPS, pp. 2787–2795 (2013)Google Scholar
  5. 5.
    Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: AAAI, pp. 301–306 (2011)Google Scholar
  6. 6.
    Carlson, A., Betteridge, J., Kisiel, B., Settles, B.: Toward an architecture for never-ending language learning. In: AAAI (2010)Google Scholar
  7. 7.
    Dong, X., et al.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: KDD, pp. 601–610. ACM (2014)Google Scholar
  8. 8.
    Jenatton, R., Roux, N.L., Bordes, A., Obozinski, G.R.: A latent factor model for highly multi-relational data. In: NIPS, pp. 3167–3175 (2012)Google Scholar
  9. 9.
    Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: Knowledge graph embedding via dynamic mapping matrix. In: ACL and IJCNLP, pp. 687–696 (2015)Google Scholar
  10. 10.
    Ji, G., Liu, K., He, S., Zhao, J.: Knowledge graph completion with adaptive sparse transfer matrix. In: AAAI, pp. 985–991 (2016)Google Scholar
  11. 11.
    Kemp, C., Tenenbaum, J.B., Griffiths, T.L., Yamada, T., Ueda, N.: Learning systems of concepts with an infinite relational model. In: AAAI, pp. 381–388. AAAI Press (2006).
  12. 12.
    Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: AAAI, pp. 2181–2187 (2015)Google Scholar
  13. 13.
    Liu, H., Wu, Y., Yang, Y.: Analogical inference for multi-relational embeddings. CoRR abs/1705.02426 (2017).
  14. 14.
    Liu, Q., Jiang, H., Ling, Z., Wei, S., Hu, Y.: Probabilistic reasoning via deep learning: neural association models. CoRR abs/1603.07704 (2016).
  15. 15.
    Momtchev, V., Peychev, D., Primov, T., Georgiev, G.: Expanding the pathway and interaction knowledge in linked life data. In: ISWC (2009)Google Scholar
  16. 16.
    Nickel, M., Rosasco, L., Poggio, T.: Holographic embeddings of knowledge graphs. In: AAAI, pp. 1955–1961 (2016)Google Scholar
  17. 17.
    Nickel, M., Tresp, V., Kriegel, H.P.: A three-way model for collective learning on multi-relational data. In: ICML, pp. 809–816 (2011)Google Scholar
  18. 18.
    Rummel, R.J.: Dimensionality of nations project: attributes of nations and behavior of nation dyads, 1950–1965 (1992).
  19. 19.
    Ruttenberg, A., Rees, J.A., Samwald, M., Marshall, M.S.: Life sciences on the Semantic Web: the Neurocommons and beyond. Brief. Bioinform. 10(2), 193–204 (2009)CrossRefGoogle Scholar
  20. 20.
    Socher, R., Chen, D., Manning, C.D., Ng, A.Y.: Reasoning with neural tensor networks for knowledge base completion. In: NIPS, pp. 926–934 (2013)Google Scholar
  21. 21.
    Suchanek, F.M., Kasneci, G., Weikum, G.: YAGO: a large ontology from Wikipedia and WordNet. Web Semant.: Sci. Serv. Agents World Wide Web 6(3), 203–217 (2008)CrossRefGoogle Scholar
  22. 22.
    Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: AAAI, pp. 1112–1119. AAAI Press (2014)Google Scholar
  23. 23.
    Welbl, J., Riedel, S., Bouchard, G.: Complex embeddings for simple link prediction. In: ICML, pp. 2071–2080 (2016)Google Scholar
  24. 24.
    Wu, W., Li, H., Wang, H., Zhu, K.Q.: Probase: a probabilistic taxonomy for text understanding. In: SIGMOD, pp. 481–492. ACM (2012)Google Scholar
  25. 25.
    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 (2014).

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Renmin University of ChinaBeijingChina

Personalised recommendations