Exception-Enriched Rule Learning from Knowledge Graphs

  • Mohamed H. Gad-Elrab
  • Daria Stepanova
  • Jacopo Urbani
  • Gerhard Weikum
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9981)

Abstract

Advances in information extraction have enabled the automatic construction of large knowledge graphs (KGs) like DBpedia, Freebase, YAGO and Wikidata. These KGs are inevitably bound to be incomplete. To fill in the gaps, data correlations in the KG can be analyzed to infer Horn rules and to predict new facts. However, Horn rules do not take into account possible exceptions, so that predicting facts via such rules introduces errors. To overcome this problem, we present a method for effective revision of learned Horn rules by adding exceptions (i.e., negated atoms) into their bodies. This way errors are largely reduced. We apply our method to discover rules with exceptions from real-world KGs. Our experimental results demonstrate the effectiveness of the developed method and the improvements in accuracy for KG completion by rule-based fact prediction.

References

  1. 1.
    Agrawal, R., Carey, M.J., Livny, M.: Concurrency control performance modeling: alternatives and implications. In: Performance of Concurrency Control Mechanisms in Centralized Database Systems, pp. 58–105 (1996)Google Scholar
  2. 2.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.G.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Azevedo, P.J., Jorge, A.M.: Comparing rule measures for predictive association rules. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 510–517. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr., E.R., Mitchell, T.M.: Toward an architecture for never-ending language learning. In: Proceedings of AAAI (2010)Google Scholar
  5. 5.
    Chen, Y., Goldberg, S., Wang, D.Z., Johri, S.S.: Ontological pathfinding: mining first-order knowledge from large knowledge bases. In: Proceedings of SIGMOD 2016, pp. 835–846 (2016)Google Scholar
  6. 6.
    Corapi, D., Russo, A., Lupu, E.: Inductive logic programming as abductive search. In: Proceedings of ICLP, pp. 54–63 (2010)Google Scholar
  7. 7.
    Darari, F., Nutt, W., Pirrò, G., Razniewski, S.: Completeness statements about RDF data sources and their use for query answering. In: Alani, H., et al. (eds.) ISWC 2013, Part I. LNCS, vol. 8218, pp. 66–83. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    Erxleben, F., Günther, M., Krötzsch, M., Mendez, J., Vrandečić, D.: Introducing wikidata to the linked data web. In: Mika, P., et al. (eds.) ISWC 2014, Part I. LNCS, vol. 8796, pp. 50–65. Springer, Heidelberg (2014)Google Scholar
  9. 9.
    Fierens, D., den Broeck, G.V., Renkens, J., Shterionov, D.S., Gutmann, B., Thon, I., Janssens, G., Raedt, L.D.: Inference and learning in probabilistic logic programs using weighted boolean formulas. TPLP 15(3), 358–401 (2015)MathSciNetGoogle Scholar
  10. 10.
    Flach, P.A., Kakas, A.C.: Abduction and Induction: Essays on Their Relation and Integration. Applied Logic Series, vol. 18. Springer, Heidelberg (2000)MATHGoogle Scholar
  11. 11.
    Galárraga, L., Teflioudi, C., Hose, K., Suchanek, F.M.: Fast rule mining in ontological knowledge bases with AMIE+. VLDB J. 24, 707–730 (2015)CrossRefGoogle Scholar
  12. 12.
    Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Proceedings of ICLP/SLP, pp. 1070–1080 (1988)Google Scholar
  13. 13.
    Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Discov. 8(1), 53–87 (2004)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Józefowska, J., Lawrynowicz, A., Lukaszewski, T.: The role of semantics in mining frequent patterns from knowledge bases in description logics with rules. TPLP 10(3), 251–289 (2010)MathSciNetMATHGoogle Scholar
  15. 15.
    Katzouris, N., Artikis, A., Paliouras, G.: Incremental learning of event definitions with inductive logic programming. Mach. Learn. 100(2–3), 555–585 (2015)MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Lassila, O., Swick, R.R.: Resource description framework (RDF) model and syntax specification (1999)Google Scholar
  17. 17.
    Law, M., Russo, A., Broda, K.: Inductive learning of answer set programs. In: Fermé, E., Leite, J. (eds.) JELIA 2014. LNCS, vol. 8761, pp. 311–325. Springer, Heidelberg (2014)Google Scholar
  18. 18.
    Lehmann, J., Auer, S., Bühmann, L., Tramp, S.: Class expression learning for ontology engineering. J. Web Sem. 9(1), 71–81 (2011)CrossRefGoogle Scholar
  19. 19.
    Leone, N., Pfeifer, G., Faber, W., Eiter, T., Gottlob, G., Perri, S., Scarcello, F.: The DLV system for knowledge representation and reasoning. ACM TOCL 7(3), 499–562 (2006)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Lisi, F.A.: Inductive logic programming in databases: from datalog to DL+log. TPLP 10(3), 331–359 (2010)MathSciNetMATHGoogle Scholar
  21. 21.
    Lloyd, J.W.: Foundations of Logic Programming, 2nd edn. Springer, Heidelberg (1987)CrossRefMATHGoogle Scholar
  22. 22.
    Mahdisoltani, F., Biega, J., Suchanek, F.M.: YAGO3: a knowledge base from multilingual Wikipedias. In: Procedings of CIDR (2015)Google Scholar
  23. 23.
    Muggleton, S., Feng, C.: Efficient induction of logic programs. In: ALT, pp. 368–381 (1990)Google Scholar
  24. 24.
    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
  25. 25.
    Nickles, M., Mileo, A.: A hybrid approach to inference in probabilistic non-monotonic logic programming. In: Proceedings of ICLP, pp. 57–68 (2015)Google Scholar
  26. 26.
    Ray, O.: Nonmonotonic abductive inductive learning. J. Appl. Logic 3(7), 329–340 (2008)MathSciNetMATHGoogle Scholar
  27. 27.
    Sakama, C.: Induction from answer sets in nonmonotonic logic programs. ACM Trans. Comput. Log. 6(2), 203–231 (2005)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Taniar, D., Rahayu, W., Lee, V., Daly, O.: Exception rules in association rule mining. Appl. Math. Comput. 205(2), 735–750 (2008)MathSciNetCrossRefMATHGoogle Scholar
  29. 29.
    Wang, Z., Li, J.: RDF2Rules: learning rules from RDF knowledge bases by mining frequent predicate cycles. CoRR abs/1512.07734 (2015)Google Scholar
  30. 30.
    Wrobel, S.: First order theory refinement. In: Raedt, L.D. (ed.) Advances in Inductive Logic Programming, pp. 14–33. IOS Press, Amsterdam (1996)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Mohamed H. Gad-Elrab
    • 1
  • Daria Stepanova
    • 1
  • Jacopo Urbani
    • 2
  • Gerhard Weikum
    • 1
  1. 1.Max Planck Institute of InformaticsSaarbrückenGermany
  2. 2.VU University AmsterdamAmsterdamThe Netherlands

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