Advertisement

PAGE: Answering Graph Pattern Queries via Knowledge Graph Embedding

  • Sanghyun Hong
  • Noseong Park
  • Tanmoy Chakraborty
  • Hyunjoong Kang
  • Soonhyun Kwon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10968)

Abstract

Answering graph pattern queries have been highly dependent on a technique—i.e., subgraph matching, however, this approach is ineffective when knowledge graphs include incorrect or incomplete information. In this paper, we present a method called \(\mathtt {PAGE}\) that answers graph pattern queries via knowledge graph embedding methods. \(\mathtt {PAGE}\) computes the energy (or uncertainty) of candidate answers with the learned embeddings and chooses the lower-energy candidates as answers. Our method has the two advantages: (1) \(\mathtt {PAGE}\) is able to find latent answers hard to be found via subgraph matching and (2) presents a robust metric that enables us to compute the plausibility of an answer. In evaluations with two popular knowledge graphs, Freebase and NELL, \(\mathtt {PAGE}\) demonstrated the performance increase by up to 28% compared to baseline KGE methods.

Keywords

Graph databases Graph query answering Knowledge graph embedding 

References

  1. 1.
    Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D., Bengio, Y.: Theano: a CPU and GPU math compiler in python. In: Proceedings of 9th Python in Science Conference, pp. 1–7 (2010)Google Scholar
  2. 2.
    Bordes, A., Glorot, X., Weston, J., Bengio, Y.: A semantic matching energy function for learning with multi-relational data - application to word-sense disambiguation. Mach. Learn. 94(2), 233–259 (2014)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: NIPS, pp. 2787–2795 (2013)Google Scholar
  4. 4.
    Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: AAAI. AAAI Press, San Francisco (2011)Google Scholar
  5. 5.
    Guo, S., Wang, Q., Wang, B., Wang, L., Guo, L.: Semantically smooth knowledge graph embedding. In: ACL, pp. 84–94. The Association for Computer Linguistics (2015)Google Scholar
  6. 6.
    Guu, K., Miller, J., Liang, P.: Traversing knowledge graphs in vector space. In: Empirical Methods in Natural Language Processing (EMNLP) (2015)Google Scholar
  7. 7.
    Hong, S., Chakraborty, T., Ahn, S., Husari, G., Park, N.: SENA: preserving social structure for network embedding. In: Proceedings of the 28th ACM Conference on Hypertext and Social Media, pp. 235–244. ACM (2017)Google Scholar
  8. 8.
    Huan, J., Wang, W., Prins, J.: Efficient mining of frequent subgraphs in the presence of isomorphism. In: Proceedings of Third IEEE International Conference on Data Mining 2003, pp. 549–552, November 2003.  https://doi.org/10.1109/ICDM.2003.1250974
  9. 9.
    Khan, A., Wu, Y., Aggarwal, C.C., Yan, X.: NeMa: fast graph search with label similarity. In: Proceedings of the 39th International Conference on Very Large Data Bases, pp. 181–192 (2013)CrossRefGoogle Scholar
  10. 10.
    Neo4j: The world’s leading graph database (2017)Google Scholar
  11. 11.
    Paulheim, H.: Knowledge graph refinement: a survey of approaches and evaluation methods. In: Semantic Web, pp. 1–20 (2016). (Preprint)CrossRefGoogle Scholar
  12. 12.
    Perozzi, B., Al-Rfou’, R., Skiena, S.: DeepWalk: online learning of social representations. In: KDD, pp. 701–710. ACM (2014)Google Scholar
  13. 13.
    Pienta, R., Tamersoy, A., Tong, H., Chau, D.H.: MAGE: matching approximate patterns in richly-attributed graphs. In: BigData Conference, pp. 585–590 (2014)Google Scholar
  14. 14.
    Prud’hommeaux, E., Seaborne, A.: SPARQL Query Language for RDF. W3C Recommendation (2008)Google Scholar
  15. 15.
    Ren, X., Wu, Z., He, W., Qu, M., Voss, C.R., Ji, H., Abdelzaher, T.F., Han, J.: CoType: joint extraction of typed entities and relations with knowledge bases. In: Proceedings of the 26th International Conference on World Wide Web, Geneva, Switzerland, pp. 1015–1024 (2017).  https://doi.org/10.1145/3038912.3052708
  16. 16.
    Ullmann, J.R.: An algorithm for subgraph isomorphism. J. ACM 23(1), 31–42 (1976).  https://doi.org/10.1145/321921.321925MathSciNetCrossRefGoogle Scholar
  17. 17.
    Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1225–1234. ACM, New York (2016).  https://doi.org/10.1145/2939672.2939753

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Sanghyun Hong
    • 1
  • Noseong Park
    • 2
  • Tanmoy Chakraborty
    • 3
  • Hyunjoong Kang
    • 4
  • Soonhyun Kwon
    • 4
  1. 1.University of MarylandCollege ParkUSA
  2. 2.University of North CarolinaCharlotteUSA
  3. 3.Indraprastha Institute of Information Technology DelhiDelhiIndia
  4. 4.Electronics and Telecommunications Research InstituteDaejeonSouth Korea

Personalised recommendations