Advertisement

Rule Induction and Reasoning over Knowledge Graphs

  • Daria Stepanova
  • Mohamed H. Gad-Elrab
  • Vinh Thinh Ho
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11078)

Abstract

Advances in information extraction have enabled the automatic construction of large knowledge graphs (KGs) like DBpedia, Freebase, YAGO and Wikidata. Learning rules from KGs is a crucial task for KG completion, cleaning and curation. This tutorial presents state-of-the-art rule induction methods, recent advances, research opportunities as well as open challenges along this avenue. We put a particular emphasis on the problems of learning exception-enriched rules from highly biased and incomplete data. Finally, we discuss possible extensions of classical rule induction techniques to account for unstructured resources (e.g., text) along with the structured ones.

References

  1. 1.
    Freebase: an open, shared database of the world’s knowledge. http://www.freebase.com/
  2. 2.
    Angluin, D.: Queries and concept learning. Mach. Learn. 2(4), 319–342 (1987)MathSciNetGoogle Scholar
  3. 3.
    Azevedo, P.J., Jorge, A.M.: Comparing rule measures for predictive association rules. In: Kok, J.N., Koronacki, J., Mantaras, R.L., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 510–517. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-74958-5_47CrossRefGoogle Scholar
  4. 4.
    Boytcheva, S.: Overview of inductive logic programming (ILP) systems (2007)Google Scholar
  5. 5.
    Chekol, M.W., Pirrò, G., Schoenfisch, J., Stuckenschmidt, H.: Marrying uncertainty and time in knowledge graphs. In: AAAI, pp. 88–94 (2017)Google Scholar
  6. 6.
    Chen, Y., Goldberg, S.L., Wang, D.Z., Johri, S.S.: Ontological pathfinding. In: SIGMOD, pp. 835–846. ACM (2016)Google Scholar
  7. 7.
    Cohen, W.W.: TensorLog: a differentiable deductive database. CoRR abs/1605.06523 (2016)Google Scholar
  8. 8.
    Corapi, D., Russo, A., Lupu, E.: Inductive logic programming as abductive search. In: ICLP, pp. 54–63 (2010)Google Scholar
  9. 9.
    Corapi, D., Russo, A., Lupu, E.: Inductive logic programming in answer set programming. In: Muggleton, S.H., Tamaddoni-Nezhad, A., Lisi, F.A. (eds.) ILP 2011. LNCS (LNAI), vol. 7207, pp. 91–97. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-31951-8_12CrossRefzbMATHGoogle Scholar
  10. 10.
    Corapi, D., Sykes, D., Inoue, K., Russo, A.: Probabilistic rule learning in nonmonotonic domains. In: Leite, J., Torroni, P., Ågotnes, T., Boella, G., van der Torre, L. (eds.) CLIMA 2011. LNCS (LNAI), vol. 6814, pp. 243–258. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-22359-4_17CrossRefzbMATHGoogle Scholar
  11. 11.
    d’Amato, C., Staab, S., Tettamanzi, A.G., Minh, T.D., Gandon, F.: Ontology enrichment by discovering multi-relational association rules from ontological knowledge bases. In: SAC, pp. 333–338 (2016)Google Scholar
  12. 12.
    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. LNCS, vol. 8218, pp. 66–83. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-41335-3_5CrossRefGoogle Scholar
  13. 13.
    De Raedt, L., Bruynooghe, M.: CLINT : a multi-strategy interactive concept-learner and theory revision system. In: Michalski, R., Tecuci, G. (eds.) Proceedings of the Multi-Strategy Learning Workshop, pp. 175–191 (1991)Google Scholar
  14. 14.
    De Raedt, L., Kimmig, A., Toivonen, H.: ProbLog: a probabilistic prolog and its application in link discovery. In: IJCAI, pp. 2468–2473 (2007)Google Scholar
  15. 15.
    Dehaspe, L., De Raedt, L.: Mining association rules in multiple relations. In: Lavrač, N., Džeroski, S. (eds.) ILP 1997. LNCS, vol. 1297, pp. 125–132. Springer, Heidelberg (1997).  https://doi.org/10.1007/3540635149_40CrossRefGoogle Scholar
  16. 16.
    Dong, X., et al.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 601–610. ACM (2014)Google Scholar
  17. 17.
    Dragoni, M., Villata, S., Rizzi, W., Governatori, G.: Combining NLP approaches for rule extraction from legal documents. In: 1st Workshop on MIning and REasoning with Legal Texts (MIREL) (2016)Google Scholar
  18. 18.
    Duc Tran, M., d’Amato, C., Nguyen, B.T., Tettamanzi, A.G.B.: Comparing rule evaluation metrics for the evolutionary discovery of multi-relational association rules in the semantic web. In: Castelli, M., Sekanina, L., Zhang, M., Cagnoni, S., García-Sánchez, P. (eds.) EuroGP 2018. LNCS, vol. 10781, pp. 289–305. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-77553-1_18CrossRefGoogle Scholar
  19. 19.
    Dzeroski, S., Lavrac, N.: Learning relations from noisy examples: An empirical comparison of LINUS and FOIL. In: ML (1991)Google Scholar
  20. 20.
    Dzyuba, V., van Leeuwen, M.: Learning what matters – sampling interesting patterns. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10234, pp. 534–546. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-57454-7_42CrossRefGoogle Scholar
  21. 21.
    Eiter, T., Ianni, G., Krennwallner, T.: Answer set programming: a primer. In: Tessaris, S. (ed.) Reasoning Web 2009. LNCS, vol. 5689, pp. 40–110. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-03754-2_2CrossRefGoogle Scholar
  22. 22.
    Eiter, T., Kaminski, T., Redl, C., Schüller, P., Weinzierl, A.: Answer set programming with external source access. In: Ianni, G., et al. (eds.) Reasoning Web 2017. LNCS, vol. 10370, pp. 204–275. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-61033-7_7CrossRefGoogle Scholar
  23. 23.
    Elkan, C., Noto, K.: Learning classifiers from only positive and unlabeled data. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 213–220 (2008)Google Scholar
  24. 24.
    Evans, R., Grefenstette, E.: Learning explanatory rules from noisy data. J. Artif. Intell. Res. 61, 1–64 (2018)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Faber, W., Pfeifer, G., Leone, N.: Semantics and complexity of recursive aggregates in answer set programming. Artif. Intell. 175(1), 278–298 (2011)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Fierens, D., et al.: Inference and learning in probabilistic logic programs using weighted boolean formulas. TPLP 15(3), 358–401 (2015)MathSciNetzbMATHGoogle Scholar
  27. 27.
    Fürnkranz, J., Gamberger, D., Lavrac, N.: Foundations of Rule Learning. Cognitive Technologies. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-540-75197-7CrossRefzbMATHGoogle Scholar
  28. 28.
    Fürnkranz, J., Kliegr, T.: A brief overview of rule learning. In: Bassiliades, N., Gottlob, G., Sadri, F., Paschke, A., Roman, D. (eds.) RuleML 2015. LNCS, vol. 9202, pp. 54–69. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-21542-6_4CrossRefGoogle Scholar
  29. 29.
    Gad-Elrab, M.H., Stepanova, D., Urbani, J., Weikum, G.: Exception-enriched rule learning from knowledge graphs. In: Groth, P., et al. (eds.) ISWC 2016. LNCS, vol. 9981, pp. 234–251. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46523-4_15CrossRefGoogle Scholar
  30. 30.
    Galarraga, L., Teflioudi, C., Hose, K., Suchanek, F.M.: Fast rule mining in ontological knowledge bases with AMIE+. VLDB 24, 707–730 (2015)CrossRefGoogle Scholar
  31. 31.
    Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Proceedings of the 5th International Conference and Symposium on Logic Programming, ICLP 1988, pp. 1070–1080 (1988)Google Scholar
  32. 32.
    Goethals, B., Van den Bussche, J.: Relational association rules: getting Warmer. In: Hand, D.J., Adams, N.M., Bolton, R.J. (eds.) Pattern Detection and Discovery. LNCS (LNAI), vol. 2447, pp. 125–139. Springer, Heidelberg (2002).  https://doi.org/10.1007/3-540-45728-3_10CrossRefGoogle Scholar
  33. 33.
    Gordon, J., Schubert, L.K.: Discovering commonsense entailment rules implicit in sentences. In: TextInfer Workshop on Textual Entailment, TIWTE 2011, pp. 59–63 (2011)Google Scholar
  34. 34.
    Ho, V.T., Stepanova, D., Gad-Elrab, M.H., Kharlamov, E., Weikum, G.: Rule learning from knowledge graphs guided by embedding models. In: ISWC 2018 (2018, in print)Google Scholar
  35. 35.
    Inoue, K., Kudoh, Y.: Learning extended logic programs. In: IJCAI, pp. 176–181. Morgan Kaufmann (1997)Google Scholar
  36. 36.
    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)MathSciNetzbMATHGoogle Scholar
  37. 37.
    Katzouris, N., Artikis, A., Paliouras, G.: Incremental learning of event definitions with inductive logic programming. Mach. Learn. 100(2–3), 555–585 (2015)MathSciNetCrossRefGoogle Scholar
  38. 38.
    Klyne, G., Carroll, J.J.: Resource description framework (RDF): concepts and abstract syntax. W3C Recommendation (2004)Google Scholar
  39. 39.
    Krogel, M.-A., Rawles, S., Železný, F., Flach, P.A., Lavrač, N., Wrobel, S.: Comparative evaluation of approaches to propositionalization. In: Horváth, T., Yamamoto, A. (eds.) ILP 2003. LNCS (LNAI), vol. 2835, pp. 197–214. Springer, Heidelberg (2003).  https://doi.org/10.1007/978-3-540-39917-9_14CrossRefGoogle Scholar
  40. 40.
    Lajus, J., Suchanek, F.M.: Are all people married?: determining obligatory attributes in knowledge bases. In: WWW, pp. 1115–1124. ACM (2018)Google Scholar
  41. 41.
    Law, M., Russo, A., Broda, K.: The ILASP system for learning answer set programs (2015). https://www.doc.ic.ac.uk/~ml1909/ILASP
  42. 42.
    Lehmann, J., et al.: DBpedia - a large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web 6, 167–195 (2015)Google Scholar
  43. 43.
    Lisi, F.A.: Inductive logic programming in databases: from datalog to DL+log. TPLP 10(3), 331–359 (2010)MathSciNetzbMATHGoogle Scholar
  44. 44.
    Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  45. 45.
    Mirza, P., Razniewski, S., Darari, F., Weikum, G.: Cardinal virtues: extracting relation cardinalities from text. In: ACL (2017)Google Scholar
  46. 46.
    Mirza, P., Razniewski, S., Nutt, W.: Expanding wikidata’s parenthood information by 178%, or how to mine relation cardinality information. In: ISWC 2016 Posters and Demos (2016)Google Scholar
  47. 47.
    Mitchell, T., et al.: Never-ending learning. In: AAAI, pp. 2302–2310 (2015)Google Scholar
  48. 48.
    Morik, K.: Balanced cooperative modeling. Mach. Learn. 11(2), 217–235 (1993)MathSciNetGoogle Scholar
  49. 49.
    Muggleton, S.: Inductive logic programming. New Gener. Comput. 8(4), 295–318 (1991)CrossRefGoogle Scholar
  50. 50.
    Muggleton, S., Buntine, W.L.: Machine invention of first order predicates by inverting resolution. In: International Conference on Machine Learning, pp. 339–352 (1988)Google Scholar
  51. 51.
    Muggleton, S., Feng, C.: Efficient induction of logic programs. In: Algorithmic Learning Theory Workshop, pp. 368–381 (1990)Google Scholar
  52. 52.
    Nakashole, N., Mitchell, T.M.: Language-aware truth assessment of fact candidates. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. 1: Long Papers, pp. 1009–1019 (2014)Google Scholar
  53. 53.
    Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E.: A review of relational machine learning for knowledge graphs: from multi-relational link prediction to automated knowledge graph construction. CoRR (2015)Google Scholar
  54. 54.
    Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E.: A review of relational machine learning for knowledge graphs. IEEE 104(1), 11–33 (2016)CrossRefGoogle Scholar
  55. 55.
    Paulheim, H.: Knowledge graph refinement: a survey of approaches and evaluation methods. Semant. Web 8(3), 489–508 (2017)CrossRefGoogle Scholar
  56. 56.
    Paulheim, H.: Learning SHACL constraints for validation of relation assertions in knowledge graphs. In: ESWC (2018, to appear)Google Scholar
  57. 57.
    Quinlan, J.R.: Learning logical definitions from relations. Mach. Learn. 5, 239–266 (1990)Google Scholar
  58. 58.
    Raedt, L.D.: Logical and Relational Learning. Cognitive Technologies. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-68856-3CrossRefzbMATHGoogle Scholar
  59. 59.
    Raedt, L.D., Dries, A., Thon, I., den Broeck, G.V., Verbeke, M.: Inducing probabilistic relational rules from probabilistic examples. In: IJCAI, pp. 1835–1843. AAAI Press (2015)Google Scholar
  60. 60.
    Raedt, L.D., Dzeroski, S.: First-order jk-clausal theories are PAC-learnable. Artif. Intell. 70(1–2), 375–392 (1994)MathSciNetCrossRefGoogle Scholar
  61. 61.
    Raedt, L.D., Lavrac, N., Dzeroski, S.: Multiple predicate learning. In: Proceedings of the 13th International Joint Conference on Artificial Intelligence, Chambéry, France, 28 August–3 September 1993, pp. 1037–1043 (1993)Google Scholar
  62. 62.
    Raedt, L.D., Passerini, A., Teso, S.: Learning constraints from examples. In: AAAI (2018)Google Scholar
  63. 63.
    De Raedt, L., Thon, I.: Probabilistic rule learning. In: Frasconi, P., Lisi, F.A. (eds.) ILP 2010. LNCS (LNAI), vol. 6489, pp. 47–58. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-21295-6_9CrossRefGoogle Scholar
  64. 64.
    Ray, O.: Nonmonotonic abductive inductive learning. J. Appl. Log. 7(3), 329–340 (2009). Special Issue: Abduction and Induction in Artificial IntelligenceMathSciNetCrossRefGoogle Scholar
  65. 65.
    Richards, B.L., Mooney, R.J.: Learning relations by pathfinding. In: Proceedings of the 10th National Conference on Artificial Intelligence, pp. 50–55 (1992)Google Scholar
  66. 66.
    Sakama, C.: Induction from answer sets in nonmonotonic logic programs. ACM Trans. Comput. Log. 6(2), 203–231 (2005)MathSciNetCrossRefGoogle Scholar
  67. 67.
    Sazonau, V., Sattler, U.: Mining hypotheses from data in OWL: advanced evaluation and complete construction. In: d’Amato, C. (ed.) ISWC 2017. LNCS, vol. 10587, pp. 577–593. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-68288-4_34CrossRefGoogle Scholar
  68. 68.
    Schoenmackers, S., Etzioni, O., Weld, D.S., Davis, J.: Learning first-order horn clauses from web text. In: EMNLP, pp. 1088–1098 (2010)Google Scholar
  69. 69.
    Shapiro, E.Y.: Algorithmic Program DeBugging. MIT Press, Cambridge (1983)zbMATHGoogle Scholar
  70. 70.
    Speck, R., Esteves, D., Lehmann, J., Ngonga Ngomo, A.C.: Defacto - a multilingual fact validation interface. In: ISWC (2015)Google Scholar
  71. 71.
  72. 72.
    Suchanek, F.M., Kasneci, G., Weikum, G.: YAGO: a core of semantic knowledge. In: Proceedings of WWW, pp. 697–706 (2007)Google Scholar
  73. 73.
    Suchanek, F.M., Preda, N.: Semantic culturomics. VLDB 7(12), 1215–1218 (2014)Google Scholar
  74. 74.
    Symeonidou, D., Galárraga, L., Pernelle, N., Saïs, F., Suchanek, F.: VICKEY: mining conditional keys on knowledge bases. In: d’Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10587, pp. 661–677. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-68288-4_39CrossRefGoogle Scholar
  75. 75.
    Tanon, T.P., Stepanova, D., Razniewski, S., Mirza, P., Weikum, G.: Completeness-aware rule learning from knowledge graphs. In: ISWC, pp. 507–525 (2017)Google Scholar
  76. 76.
    Tran, H.D., Stepanova, D., Gad-Elrab, M.H., Lisi, F.A., Weikum, G.: Towards nonmonotonic relational learning from knowledge graphs. In: Cussens, J., Russo, A. (eds.) ILP 2016. LNCS (LNAI), vol. 10326, pp. 94–107. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-63342-8_8CrossRefGoogle Scholar
  77. 77.
    Vrandecic, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. CACM 57(10), 78–85 (2014)CrossRefGoogle Scholar
  78. 78.
    Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications, pp. 2724–2743 (2017)Google Scholar
  79. 79.
    Wang, Z., Li, J.: RDF2Rules: learning rules from RDF knowledge bases by mining frequent predicate cycles. CoRR abs/1512.07734 (2015)Google Scholar
  80. 80.
    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)Google Scholar
  81. 81.
    Yang, F., Yang, Z., Cohen, W.W.: Differentiable learning of logical rules for knowledge base reasoning. In: NIPS, pp. 2316–2325 (2017)Google Scholar
  82. 82.
    Zupanc, K., Davis, J.: Estimating rule quality for knowledge base completion with the relationship between coverage assumption. In: WWW, pp. 1073–1081 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Daria Stepanova
    • 1
  • Mohamed H. Gad-Elrab
    • 1
  • Vinh Thinh Ho
    • 1
  1. 1.Max Planck Institute for InformaticsSaarbrückenGermany

Personalised recommendations