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Statistical Relational Learning

  • Chapter
Handbook on Neural Information Processing

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 49))

Abstract

Relational learning refers to learning from data that have a complex structure. This structure may be either internal (a data instance may itself have a complex structure) or external (relationships between this instance and other data elements). Statistical relational learning refers to the use of statistical learning methods in a relational learning context, and the challenges involved in that. In this chapter we give an overview of statistical relational learning. We start with some motivating problems, and continue with a general description of the task of (statistical) relational learning and some of its more concrete forms (learning from graphs, learning from logical interpretations, learning from relational databases). Next, we discuss a number of approaches to relational learning, starting with symbolic (non-probabilistic) approaches, and moving on to numerical and probabilistic methods. Methods discussed include inductive logic programming, relational neural networks, and probabilistic logical or relational models

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References

  1. Blockeel, H., Bruynooghe, M.: Aggregation versus selection bias, and relational neural networks. In: IJCAI 2003 Workshop on Learning Statistical Models from Relational Data, SRL 2003, Acapulco, Mexico, August 11 (2003)

    Google Scholar 

  2. Bratko, I.: Prolog Programming for Artificial Intelligence. Addison-Wesley (1986)

    Google Scholar 

  3. Bruynooghe, M., De Cat, B., Drijkoningen, J., Fierens, D., Goos, J., Gutmann, B., Kimmig, A., Labeeuw, W., Langenaken, S., Landwehr, N., Meert, W., Nuyts, E., Pellegrims, R., Rymenants, R., Segers, S., Thon, I., Van Eyck, J., Van den Broeck, G., Vangansewinkel, T., Van Hove, L., Vennekens, J., Weytjens, T., De Raedt, L.: An exercise with statistical relational learning systems. In: Proceedings of the 6th International Workshop on Statistical Relational Learning (2009)

    Google Scholar 

  4. Bruynooghe, M., Mantadelis, T., Kimmig, A., Gutmann, B., Vennekens, J., Janssens, G., De Raedt, L.: Problog technology for inference in a probabilistic first order logic. In: Coelho, H., Studer, R., Wooldridge, M. (eds.) ECAI. Frontiers in Artificial Intelligence and Applications, vol. 215, pp. 719–724. IOS Press (2010)

    Google Scholar 

  5. Buntine, W.: Operations for learning with graphical models. Journal of Artificial Intelligence Research 2, 159–225 (1994)

    Google Scholar 

  6. Cook, D.J., Holder, L.B.: Substructure discovery using minimum description length and background knowledge. J. Artif. Intell. Res. (JAIR) 1, 231–255 (1994)

    Google Scholar 

  7. Cook, D.J., Holder, L.B.: Mining Graph Data. Wiley (2007)

    Google Scholar 

  8. d’Avila Garcez, A.S., Zaverucha, G.: The connectionist inductive learning and logic programming system. Appl. Intell. 11(1), 59–77 (1999)

    Article  Google Scholar 

  9. De Raedt, L.: Logical settings for concept learning. Artificial Intelligence 95, 187–201 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  10. De Raedt, L.: Logical and Relational Learning. Springer (2008)

    Google Scholar 

  11. De Raedt, L., Dehaspe, L.: Clausal discovery. Machine Learning 26, 99–146 (1997)

    Article  MATH  Google Scholar 

  12. De Raedt, L., Dehaspe, L.: Clausal discovery. Machine Learning 26(2-3), 99–146 (1997)

    Article  MATH  Google Scholar 

  13. De Raedt, L., Demoen, B., Fierens, D., Gutmann, B., Janssens, G., Kimmig, A., Landwehr, N., Mantadelis, T., Meert, W., Rocha, R., Santos Costa, V., Thon, I., Vennekens, J.: Towards digesting the alphabet-soup of statistical relational learning. In: Proceedings of the NIPS*2008 Workshop Probabilistic Programming, pp. 1–3 (2008)

    Google Scholar 

  14. De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S.H. (eds.): Probabilistic Inductive Logic Programming. LNCS (LNAI), vol. 4911. Springer, Heidelberg (2008)

    MATH  Google Scholar 

  15. De Raedt, L., Kersting, K.: Probabilistic logic learning. SIGKDD Explorations 5(1), 31–48 (2003)

    Article  Google Scholar 

  16. Dehaspe, L., Toivonen, H.: Discovery of frequent datalog patterns. Data Mining and Knowledge Discovery 3(1), 7–36 (1999)

    Article  Google Scholar 

  17. Džeroski, S., De Raedt, L., Driessens, K.: Relational reinforcement learning. Mach. Learn. 43, 7–52 (2001)

    Article  MATH  Google Scholar 

  18. Džeroski, S., Lavrač, N. (eds.): Relational Data Mining. Springer (2001)

    Google Scholar 

  19. Fierens, D., Blockeel, H., Bruynooghe, M., Ramon, J.: Logical bayesian networks and their relation to other probabilistic logical models. In: Kramer, Pfahringer [38], pp. 121–135

    Google Scholar 

  20. Fierens, D., Ramon, J., Bruynooghe, M., Blockeel, H.: Learning Directed Probabilistic Logical Models: Ordering-Search Versus Structure-Search. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 567–574. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  21. Finn, P., Muggleton, S., Page, D., Srinivasan, A.: Pharmacophore discovery using the inductive logic programming system Progol. Mach. Learn. 30, 241–270 (1998)

    Article  Google Scholar 

  22. Flach, P.A., Lachiche, N.: Naive bayesian classification of structured data. Machine Learning 57(3), 233–269 (2004)

    Article  MATH  Google Scholar 

  23. Friedman, N., Getoor, L., Koller, D., Pfeffer, A.: Learning probabilistic relational models. In: Dean, T. (ed.) IJCAI, pp. 1300–1309. Morgan Kaufmann (1999)

    Google Scholar 

  24. Getoor, L., Taskar, B.: Introduction to Statistical Relational Learning. MIT Press (2007)

    Google Scholar 

  25. Gilks, W.R., Thomas, A., Spiegelhalter, D.J.: A language and program for complex bayesian modelling. The Statistician 43, 169–178 (1994)

    Article  Google Scholar 

  26. Haddawy, P.: Generating bayesian networks from probablity logic knowledge bases. In: de Mántaras, R.L., Poole, D. (eds.) UAI, pp. 262–269. Morgan Kaufmann (1994)

    Google Scholar 

  27. Halpern, J.Y.: An analysis of first-order logics of probability. Artificial Intelligence 46, 311–350 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  28. Heckerman, D., Meek, C., Koller, D.: Probabilistic entity-relationship models, prms, and plate models. In: Introduction to Statistical Relational Learning, pp. 201–238. MIT Press (2007)

    Google Scholar 

  29. Horváth, T., Ramon, J., Wrobel, S.: Frequent subgraph mining in outerplanar graphs. In: Proc. of the 12th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining, pp. 197–206 (2006)

    Google Scholar 

  30. Jaeger, M.: Relational bayesian networks. In: UAI 1997: Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, Brown University, Providence, Rhode Island, USA, August 1-3, pp. 266–273. Morgan Kaufmann (1997)

    Google Scholar 

  31. Jensen, D., Neville, J.: Linkage and autocorrelation cause feature selection bias in relational learning. In: Proc. of the 19th Int’l Conf. on Machine Learning, pp. 259–266 (2002)

    Google Scholar 

  32. Jensen, D., Neville, J., Gallagher, B.: Why collective inference improves relational classification. In: Proc. of the 10th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining, pp. 593–598 (2004)

    Google Scholar 

  33. Karalič, A., Bratko, I.: First order regression. Machine Learning 26, 147–176 (1997)

    Article  MATH  Google Scholar 

  34. Kersting, K.: An Inductive Logic Programming Approach to Statistical Relational Learning. IOS Press (2006)

    Google Scholar 

  35. Kersting, K., Dick, U.: Balios – The Engine for Bayesian Logic Programs. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, pp. 549–551. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  36. Kimmig, A., De Raedt, L.: Local query mining in a probabilistic prolog. In: Boutilier, C. (ed.) IJCAI, pp. 1095–1100 (2009)

    Google Scholar 

  37. Koller, D., Friedman, N., Getoor, L., Taskar, B.: Graphical models in a nutshell. In: Introduction to Statistical Relational Learning, pp. 13–55. MIT Press (2007)

    Google Scholar 

  38. Kramer, S., Pfahringer, B. (eds.): ILP 2005. LNCS (LNAI), vol. 3625. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  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)

    Chapter  Google Scholar 

  40. Krogel, M.-A., Wrobel, S.: Transformation-Based Learning Using Multirelational Aggregation. In: Rouveirol, C., Sebag, M. (eds.) ILP 2001. LNCS (LNAI), vol. 2157, pp. 142–155. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  41. Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Brodley, C.E., Danyluk, A.P. (eds.) ICML, pp. 282–289. Morgan Kaufmann (2001)

    Google Scholar 

  42. Lloyd, J.W.: Logic for Learning. Springer (2003)

    Google Scholar 

  43. Meert, W., Struyf, J., Blockeel, H.: Learning ground CP-Logic theories by leveraging bayesian network learning techniques. Fundam. Inform. 89(1), 131–160 (2008)

    MathSciNet  MATH  Google Scholar 

  44. Milch, B., Marthi, B., Russell, S.J., Sontag, D., Ong, D.L., Kolobov, A.: Blog: Probabilistic models with unknown objects. In: Kaelbling, L.P., Saffiotti, A. (eds.) IJCAI, pp. 1352–1359. Professional Book Center (2005)

    Google Scholar 

  45. Muggleton, S.: Inverse entailment and Progol. New Generation Computing, Special issue on Inductive Logic Programming 13(3-4), 245–286 (1995)

    Article  Google Scholar 

  46. Muggleton, S.: Stochastic logic programs. In: De Raedt, L. (ed.) Advances in Inductive Logic Programming, pp. 254–264. IOS Press (1996)

    Google Scholar 

  47. Nijssen, S., Kok, J.N.: The gaston tool for frequent subgraph mining. Electr. Notes Theor. Comput. Sci. 127(1), 77–87 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  48. Perlich, C., Provost, F.J.: Aggregation-based feature invention and relational concept classes. In: Getoor, L., Senator, T.E., Domingos, P., Faloutsos, C. (eds.) KDD, pp. 167–176. ACM (2003)

    Google Scholar 

  49. Pfeffer, A.: The design and implementation of ibal: A general-purpose probabilistic programming language. Technical Report TR-12-05, Harvard University (2005)

    Google Scholar 

  50. Poole, D.: First-order probabilistic inference. In: Gottlob, G., Walsh, T. (eds.) IJCAI, pp. 985–991. Morgan Kaufmann (2003)

    Google Scholar 

  51. Quinlan, J.R.: Learning logical definitions from relations. Machine Learning 5, 239–266 (1990)

    Google Scholar 

  52. Richardson, M., Domingos, P.: Markov logic networks. Mach. Learn. 62(1-2), 107–136 (2006)

    Article  Google Scholar 

  53. Santos Costa, V., Page, D., Qazi, M., Cussens, J.: Clp(bn): Constraint logic programming for probabilistic knowledge. In: Meek, C., Kjærulff, U. (eds.) UAI, pp. 517–524. Morgan Kaufmann (2003)

    Google Scholar 

  54. Sato, T., Kameya, Y.: PRISM: A symbolic-statistical modeling language. In: Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI 1997), pp. 1330–1335 (1997)

    Google Scholar 

  55. Spiegelhalter, D.J.: Bayesian graphical modelling: a case-study in monitoring health outcomes. Applied Statistics 47, 115–134 (1998)

    Google Scholar 

  56. Struyf, J., Blockeel, H.: Relational learning. In: Sammut, C., Webb, G. (eds.) Encyclopedia of Machine Learning, pp. 851–857. Springer (2010)

    Google Scholar 

  57. Tadepalli, P., Givan, R., Driessens, K.: Relational reinforcement learning: An overview. In: Proc. of the ICML 2004 Wshp. on Relational Reinforcement Learning, pp. 1–9 (2004)

    Google Scholar 

  58. Taghipour, N., Fierens, D., Blockeel, H.: Probabilistic logical learning for biclustering: A case study with surprising results. CW Reports CW597, Department of Computer Science, K.U.Leuven (October 2010)

    Google Scholar 

  59. Thon, I., Landwehr, N., De Raedt, L.: A Simple Model for Sequences of Relational State Descriptions. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part II. LNCS (LNAI), vol. 5212, pp. 506–521. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  60. Towell, G.G., Shavlik, J.W.: Knowledge-based artificial neural networks. Artif. Intell. 70(1-2), 119–165 (1994)

    Article  MATH  Google Scholar 

  61. Uwents, W.: Learning complex aggregate features with relational neural networks. PhD thesis, Katholieke Universiteit Leuven (2011) (forthcoming)

    Google Scholar 

  62. Uwents, W., Blockeel, H.: Classifying relational data with neural networks. In: Kramer, Pfahringer [38], pp. 384–396

    Google Scholar 

  63. Uwents, W., Blockeel, H.: A Comparison between Neural Network Methods for Learning Aggregate Functions. In: Boulicaut, J.-F., Berthold, M.R., Horváth, T. (eds.) DS 2008. LNCS (LNAI), vol. 5255, pp. 88–99. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  64. Uwents, W., Blockeel, H.: Learning Aggregate Functions with Neural Networks Using a Cascade-Correlation Approach. In: Železný, F., Lavrač, N. (eds.) ILP 2008. LNCS (LNAI), vol. 5194, pp. 315–329. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  65. Uwents, W., Monfardini, G., Blockeel, H., Gori, M., Scarselli, F.: Neural networks for relational learning: An experimental comparison. Machine Learning 82, 315–349 (2011)

    Article  Google Scholar 

  66. Van Assche, A., Vens, C., Blockeel, H., Džeroski, S.: First order random forests: Learning relational classifiers with complex aggregates. Machine Learning 64(1-3), 149–182 (2006)

    Article  MATH  Google Scholar 

  67. Vennekens, J., Denecker, M., Bruynooghe, M.: Cp-logic: A language of causal probabilistic events and its relation to logic programming. TPLP 9(3), 245–308 (2009)

    MathSciNet  MATH  Google Scholar 

  68. Vennekens, J., Verbaeten, S., Bruynooghe, M.: Logic Programs with Annotated Disjunctions. In: Demoen, B., Lifschitz, V. (eds.) ICLP 2004. LNCS, vol. 3132, pp. 431–445. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  69. Vens, C., Ramon, J., Blockeel, H.: Refining Aggregate Conditions in Relational Learning. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 383–394. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  70. Washio, T., Motoda, H.: State of the art of graph-based data mining. SIGKDD Explorations 5(1), 59–68 (2003)

    Article  Google Scholar 

  71. Yan, X., Han, J.: gspan: Graph-based substructure pattern mining. In: ICDM, pp. 721–724. IEEE Computer Society (2002)

    Google Scholar 

  72. Yin, X., Han, J., Yang, J., Yu, P.S.: Efficient classification across multiple database relations: A CrossMine approach. IEEE Trans. Knowl. Data Eng. 18(6), 770–783 (2006)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, KU Leuven, Leuven, Belgium

    Hendrik Blockeel

  2. Leiden Institute of Advanced Computer Science, Leiden, The Netherlands

    Hendrik Blockeel

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  1. Hendrik Blockeel
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  1. , Dipto. Ingegneria dell'Informazione, Università degli Studi di Siena, Via Roma 56, Siena, 53100, Italy

    Monica Bianchini

  2. Fac. Ingegneria, Dipto. Ingegneria dell'Informazione, Università Siena, Via Roma 56, Siena, 53100, Italy

    Marco Maggini

  3. University of Canberra, School of Electrical and Information, Adjunct Professor, Mawson Lakes Campus, ACT, 2601, Australia

    Lakhmi C. Jain

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Blockeel, H. (2013). Statistical Relational Learning. In: Bianchini, M., Maggini, M., Jain, L. (eds) Handbook on Neural Information Processing. Intelligent Systems Reference Library, vol 49. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36657-4_8

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  • DOI: https://doi.org/10.1007/978-3-642-36657-4_8

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