Identifying Equivalent Relation Paths in Knowledge Graphs

  • Sameh K. Mohamed
  • Emir MuñozEmail author
  • Vít Nováček
  • Pierre-Yves Vandenbussche
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10318)


Relation paths are sequences of relations with inverse that allow for complete exploration of knowledge graphs in a two-way unconstrained manner. They are powerful enough to encode complex relationships between entities and are crucial in several contexts, such as knowledge base verification, rule mining, and link prediction. However, fundamental forms of reasoning such as containment and equivalence of relation paths have hitherto been ignored. Intuitively, two relation paths are equivalent if they share the same extension, i.e., set of source and target entity pairs. In this paper, we study the problem of containment as a means to find equivalent relation paths and show that it is very expensive in practice to enumerate paths between entities. We characterize the complexity of containment and equivalence of relation paths and propose a domain-independent and unsupervised method to obtain approximate equivalences ranked by a tri-criteria ranking function. We evaluate our algorithm using test cases over real-world data and show that we are able to find semantically meaningful equivalences efficiently.


  1. 1.
    Abiteboul, S., Hull, R., Vianu, V. (eds.): Foundations of Databases: The Logical Level, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston (1995)Google Scholar
  2. 2.
    Baeza, P.B.: Querying graph databases. In: PODS, pp. 175–188. ACM (2013)Google Scholar
  3. 3.
    Calvanese, D., De Giacomo, G., Lenzerini, M., Vardi, M.Y.: Containment of conjunctive regular path queries with inverse. In: KR, pp. 176–185. Morgan Kaufmann (2000)Google Scholar
  4. 4.
    Calvanese, D., De Giacomo, G., Lenzerini, M., Vardi, M.Y.: Reasoning on regular path queries. SIGMOD Rec. 32(4), 83–92 (2003)CrossRefGoogle Scholar
  5. 5.
    Chandra, A.K., Merlin, P.M.: Optimal implementation of conjunctive queries in relational data bases. In: STOC, pp. 77–90. ACM (1977)Google Scholar
  6. 6.
    Consens, M.P., Mendelzon, A.O.: GraphLog: a visual formalism for real life recursion. In: PODS, pp. 404–416. ACM Press (1990)Google Scholar
  7. 7.
    Dong, X., Gabrilovich, E., Heitz, G., Horn, W., Lao, N., Murphy, K., Strohmann, T., Sun, S., Zhang, W.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: KDD, pp. 601–610. ACM (2014)Google Scholar
  8. 8.
    Freitas, A., da Silva, J.C.P., Curry, E., Buitelaar, P.: A distributional semantics approach for selective reasoning on commonsense graph knowledge bases. In: NLDB, Montpellier, France, 18–20 June 2014, pp. 21–32 (2014)Google Scholar
  9. 9.
    Galárraga, L., Teflioudi, C., Hose, K., Suchanek, F.M.: Fast rule mining in ontological knowledge bases with AMIE+. VLDB J. 24(6), 707–730 (2015)CrossRefGoogle Scholar
  10. 10.
    Kostylev, E.V., Reutter, J.L., Romero, M., Vrgoč, D.: SPARQL with property paths. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 3–18. Springer, Cham (2015). doi: 10.1007/978-3-319-25007-6_1 CrossRefGoogle Scholar
  11. 11.
    Lao, N., Cohen, W.W.: Relational retrieval using a combination of path-constrained random walks. Mach. Learn. 81(1), 53–67 (2010)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Lao, N., Subramanya, A., Pereira, F.C.N., Cohen, W.W.: Reading the web with learned syntactic-semantic inference rules. In: EMNLP-CoNLL, pp. 1017–1026. ACL (2012)Google Scholar
  13. 13.
    Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., Bizer, C.: DBpedia - a large-scale, multilingual knowledge base extracted from Wikipedia. Seman. Web 6(2), 167–195 (2015)Google Scholar
  14. 14.
    Lin, X., Liang, Y., Guan, R.: Compositional learning of relation paths embedding for knowledge base completion. CoRR abs/1611.07232 (2016)Google Scholar
  15. 15.
    Lin, Y., Liu, Z., Sun, M.: Modeling relation paths for representation learning of knowledge bases. CoRR abs/1506.00379 (2015)Google Scholar
  16. 16.
    Mahdisoltani, F., Biega, J., Suchanek, F.M.: YAGO3: a knowledge base from multilingual wikipedias. In: CIDR (2015).
  17. 17.
    Mendelzon, A.O., Wood, P.T.: Finding regular simple paths in graph databases. SIAM J. Comput. 24(6), 1235–1258 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  19. 19.
    Mitchell, T.M., Cohen Jr., W.W., Hruschka, E.R., Talukdar, P.P., Betteridge, J., Carlson, A., Mishra, B.D., Gardner, M., Kisiel, B., Krishnamurthy, J., Lao, N., Mazaitis, K., Mohamed, T., Nakashole, N., Platanios, E.A., Ritter, A., Samadi, M., Settles, B., Wang, R.C., Wijaya, D.T., Gupta, A., Chen, X., Saparov, A., Greaves, M., Welling, J.: Never-ending learning. In: AAAI, pp. 2302–2310. AAAI Press (2015)Google Scholar
  20. 20.
    Morzy, M., Ławrynowicz, A., Zozuliński, M.: Using substitutive itemset mining framework for finding synonymous properties in linked data. In: Bassiliades, N., Gottlob, G., Sadri, F., Paschke, A., Roman, D. (eds.) RuleML 2015. LNCS, vol. 9202, pp. 422–430. Springer, Cham (2015). doi: 10.1007/978-3-319-21542-6_27 CrossRefGoogle Scholar
  21. 21.
    Pichler, R., Skritek, S.: Containment and equivalence of well-designed SPARQL. In: PODS, pp. 39–50. ACM (2014)Google Scholar
  22. 22.
    Vardi, M.Y.: A note on the reduction of two-way automata to one-way automata. Inf. Process. Lett. 30(5), 261–264 (1989)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Zhang, Z., Gentile, A.L., Augenstein, I., Blomqvist, E., Ciravegna, F.: Mining equivalent relations from linked data. In: ACL, vol. 2, pp. 289–293. The Association for Computer Linguistics (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sameh K. Mohamed
    • 1
  • Emir Muñoz
    • 1
    • 2
    Email author
  • Vít Nováček
    • 1
  • Pierre-Yves Vandenbussche
    • 2
  1. 1.Insight Centre for Data Analytics at NUIGalwayIreland
  2. 2.Fujitsu Ireland LimitedDublinIreland

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