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Approximate Relational Reasoning by Stochastic Propositionalization

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Advances in Intelligent Information Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 265))

Abstract

For many real-world applications it is important to choose the right representation language. While the setting of First Order Logic (FOL) is the most suitable one to model the multi-relational data of real and complex domains, on the other hand it puts the question of the computational complexity of the knowledge induction process. A way of tackling the complexity of such real domains, in which a lot of relationships are required to model the objects involved, is to use a method that reformulates a multi-relational learning task into an attribute-value one. In this chapter we present an approximate reasoning method able to keep low the complexity of a relational problem by using a stochastic inference procedure. The complexity of the relational language is decreased by means of a propositionalization technique, while the NP-completeness of the deduction is tackled using an approximate query evaluation. The proposed approximate reasoning technique has been used to solve the problem of relational rule induction as well as the task of relational clustering. An anytime algorithm has been used for the induction, implemented by a population based method, able to efficiently extract knowledge from relational data, while the clustering task, both unsupervised and supervised, has been solved using a Partition Around Medoid (PAM) clustering algorithm. The validity of the proposed techniques has been proved making an empirical evaluation on real-world datasets.

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References

  1. Alphonse, É., Matwin, S.: A dynamic approach to dimensionality reduction in relational learning. In: Hacid, M.-S., Raś, Z.W., Zighed, D.A., Kodratoff, Y. (eds.) ISMIS 2002. LNCS (LNAI), vol. 2366, pp. 255–679. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  2. Alphonse, E., Rouveirol, C.: Lazy propositionalization for relational learning. In: Horn, W. (ed.) Proc. of the 14th European Conference on Artificial Intelligence, pp. 256–260. IOS Press, Amsterdam (2000)

    Google Scholar 

  3. Anderson, G., Pfahringer, B.: Clustering relational data based on randomized propositionalization. In: Blockeel, H., Ramon, J., Shavlik, J., Tadepalli, P. (eds.) ILP 2007. LNCS (LNAI), vol. 4894, pp. 39–48. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  4. Bisson, G.: Learning in FOL with a similarity measure. In: Proceedings of the 10th National Conference on Artificial Intelligence, pp. 82–87 (1992)

    Google Scholar 

  5. Blockeel, H., Raedt, L.D., Ramon, J.: Top-down induction of clustering trees. In: Proceedings of the 15th International Conference on Machine Learning, pp. 55–63. Morgan Kaufmann, San Francisco (1998)

    Google Scholar 

  6. Boddy, M., Dean, T.L.: Deliberation scheduling for problem solving in time-constrained environments. Artificial Intelligence 67, 245–285 (1994)

    Article  MATH  Google Scholar 

  7. Bohnebeck, U., Horváth, T., Wrobel, S.: Term comparisons in first-order similarity measures. In: Proceedings of the 8th International Workshop on Inductive Logic Programming, pp. 65–79. Springer, Heidelberg (1998)

    Google Scholar 

  8. Bratko, I.: Prolog programming for artificial intelligence, 3rd edn. Addison-Wesley Longman Publishing Co., Amsterdam (2001)

    Google Scholar 

  9. Di Mauro, N., Basile, T.M.A., Ferilli, S., Esposito, F.: Approximate reasoning for efficient anytime induction from relational knowledge bases. In: Greco, S., Lukasiewicz, T. (eds.) SUM 2008. LNCS (LNAI), vol. 5291, pp. 160–173. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  10. Di Mauro, N., Basile, T.M.A., Ferilli, S., Esposito, F.: Stochastic propositionalization for efficient multi-relational learning. In: An, A., Matwin, S., Raś, Z.W., Ślęzak, D. (eds.) Foundations of Intelligent Systems. LNCS (LNAI), vol. 4994, pp. 78–83. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  11. Dietterich, T.G., Lathrop, R.H., Lozano-Perez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence 89(1-2), 31–71 (1997)

    Article  MATH  Google Scholar 

  12. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley Interscience, Hoboken (2000)

    Google Scholar 

  13. Eick, C.F., Zeidat, N., Zhao, Z.: Supervised clustering: Algorithms and benefits. In: ICTAI 2004: Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence, pp. 774–776. IEEE Computer Society Press, Los Alamitos (2004)

    Google Scholar 

  14. Esposito, F., Ferilli, S., Basile, T.M.A., Di Mauro, N.: Machine learning for digital document processing: From layout analysis to metadata extraction. In: Marinai, S., Fujisawa, H. (eds.) Machine Learning in Document Analysis and Recognition. Studies in Computational Intelligence, vol. 90, pp. 105–138. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  15. Giordana, A., Botta, M., Saitta, L.: An experimental study of phase transitions in matching. In: Thomas, D. (ed.) Proceedings of the 16th International Joint Conference on Artificial Intelligence. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  16. Kaufman, L., Rousseeuw, P.: Finding groups in data: an introduction to cluster analysis. John Wiley and Sons, Chichester (1990)

    Google Scholar 

  17. Kramer, S., Pfahringer, B., Helma, C.: Stochastic propositionalization of non-determinate background knowledge. In: Proceedings of the 8th International Workshop on Inductive Logic Programming, pp. 80–94. Springer, Heidelberg (1998)

    Google Scholar 

  18. Krogel, M.A., Rawles, S., Zelezny, F., Flach, P., Lavrac, N., Wrobel, S.: Comparative evaluation of approaches to propositionalization. In: Horváth, T., Yamamoto, A. (eds.) ILP 2003. LNCS (LNAI), vol. 2835, pp. 194–217. Springer, Heidelberg (2003)

    Google Scholar 

  19. Lavrac, N., Dzeroski, S.: Inductive Logic Programming: Techniques and Applications. Ellis Horwood, New York (1994)

    MATH  Google Scholar 

  20. Lavrac, N., Dzeroski, S., Grobelnik, M.: Learning nonrecursive definitions of relations with linus. In: Proceedings of the European Working Session on Machine Learning, pp. 265–281. Springer, Heidelberg (1991)

    Google Scholar 

  21. Lavrač, N., Železný, F., Flach, P.A.: RSD: Relational subgroup discovery through first-order feature construction. In: Matwin, S., Sammut, C. (eds.) ILP 2002. LNCS (LNAI), vol. 2583, pp. 149–165. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

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

    Google Scholar 

  23. Muggleton, S., De Raedt, L.: Inductive logic programming: Theory and methods. Journal of Logic Programming 19/20, 629–679 (1994)

    Article  MathSciNet  Google Scholar 

  24. Nienhuys-Cheng, S.-H.: Distances and limits on herbrand interpretations. In: Proceedings of the 8th International Workshop on Inductive Logic Programming, pp. 250–260. Springer, Heidelberg (1998)

    Google Scholar 

  25. Plotkin, G.D.: A note on inductive generalization. In: Meltzer, B., Michie, D. (eds.) Machine Intelligence, ch. 8, vol. 5, pp. 153–163. Edinburg Univ. Press (1970)

    Google Scholar 

  26. Raedt, L.D.: Attribute value learning versus inductive logic programming: The missing links (extended abstract). In: Page, D.L. (ed.) ILP 1998. LNCS, vol. 1446, pp. 1–8. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  27. Sebag, M.: Distance induction in first order logic. In: Proceedings of the 7th International Workshop on Inductive Logic Programming, pp. 264–272. Springer, Heidelberg (1997)

    Google Scholar 

  28. Sebag, M., Rouveirol, C.: Induction of maximally general clauses consistent with integrity constraints. In: Wrobel, S. (ed.) Proceedings of the 4th International Workshop on Inductive Logic Programming. GMD-Studien, vol. 237, pp. 195–216. Gesellschaft für Mathematik und Datenverarbeitung MBH (1994)

    Google Scholar 

  29. Sebag, M., Rouveirol, C.: Tractable induction and classification in first order logic via stochastic matching. In: 15th International Join Conference on Artificial Intelligence, pp. 888–893. Morgan Kaufmann, San Francisco (1997)

    Google Scholar 

  30. Srinivasan, A., Muggleton, S., King, R.: Comparing the use of background knowledge by inductive logic programming systems. In: Raedt, L.D. (ed.) Proceedings of the 5th International Workshop on Inductive Logic Programming, pp. 199–230. Springer, Heidelberg (1995)

    Google Scholar 

  31. Ullman, J.: Principles of Database and Knowledge-Base Systems, vol. I. Computer Science Press (1988)

    Google Scholar 

  32. Zilberstein, S., Russell, S.: Approximate reasoning using anytime algorithms. Imprecise and Approximate Computation 318, 43–62 (1995)

    Article  Google Scholar 

  33. Zucker, J.-D., Ganascia, J.-G.: Representation changes for efficient learning in structural domains. In: Proceedings of 13th International Conference on Machine Learning, pp. 543–551. Morgan Kaufmann, San Francisco (1996)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department Of Computer Science, University of Bari, Italy

    Nicola Di Mauro, Teresa M. A. Basile, Stefano Ferilli & Floriana Esposito

Authors
  1. Nicola Di Mauro
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  2. Teresa M. A. Basile
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  3. Stefano Ferilli
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  4. Floriana Esposito
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of North Carolina, N.C. 28223, Charlotte, USA

    Zbigniew W. Ras

  2. Department of Electronics, Computer & Information Technology, NC A &T State University, NC 27411, Greensboro, USA

    Li-Shiang Tsay

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Di Mauro, N., Basile, T.M.A., Ferilli, S., Esposito, F. (2010). Approximate Relational Reasoning by Stochastic Propositionalization. In: Ras, Z.W., Tsay, LS. (eds) Advances in Intelligent Information Systems. Studies in Computational Intelligence, vol 265. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05183-8_4

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  • DOI: https://doi.org/10.1007/978-3-642-05183-8_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05182-1

  • Online ISBN: 978-3-642-05183-8

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