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International Conference on Inductive Logic Programming

ILP 2006: Inductive Logic Programming pp 184–198Cite as

Efficient and Scalable Induction of Logic Programs Using a Deductive Database System

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4455))

Abstract

A consequence of ILP systems being implemented in Prolog or using Prolog libraries is that, usually, these systems use a Prolog internal database to store and manipulate data. However, in real-world problems, the original data is rarely in Prolog format. In fact, the data is often kept in Relational Database Management Systems (RDBMS) and then converted to a format acceptable by the ILP system. Therefore, a more interesting approach is to link the ILP system to the RDBMS and manipulate the data without converting it. This scheme has the advantage of being more scalable since the whole data does not need to be loaded into memory by the ILP system. In this paper we study several approaches of coupling ILP systems with RDBMS systems and evaluate their impact on performance. We propose to use a Deductive Database (DDB) system to transparently translate the hypotheses to relational algebra expressions. The empirical evaluation performed shows that the execution time of ILP algorithms can be effectively reduced using a DDB and that the size of the problems can be increased due to a non-memory storage of the data.

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References

  1. Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E.: The Protein Data Bank. Nucleic Acids Research, 235–242 (2000)

    Google Scholar 

  2. Benson, D., Karsch-Mizrachi, I., Lipman, D., Ostell, J., Wheeler, D.: GenBank. Nucleic Acids Research 33, 235–242 (2005)

    Article  Google Scholar 

  3. Wrobel, S.: Inductive Logic Programming for Knowledge Discovery in Databases. In: Relational Data Mining, pp. 74–101. Springer, Heidelberg (2001)

    Google Scholar 

  4. Raedt, L.D.: Attribute Value Learning versus Inductive Logic Programming: The Missing Links. In: Page, D.L. (ed.) Inductive Logic Programming. LNCS, vol. 1446, pp. 1–8. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  5. Raedt, L.D., Laer, W.V.: Inductive Constraint Logic. In: International Conference on Algorithmic Learning Theory, pp. 80–94. Springer, Heidelberg (1995)

    Google Scholar 

  6. Raedt, L.D., Dehaspe, L.: Clausal Discovery. Machine Learning 26, 99–146 (1997)

    Article  MATH  Google Scholar 

  7. Srinivasan, A.: The Aleph Manual (2003), available from http://web.comlab.ox.ac.uk/oucl/research/areas/machlearn/Aleph

  8. Muggleton, S., Firth, J.: Relational Rule Induction with CProgol4.4: A Tutorial Introduction. In: Relational Data Mining, pp. 160–188. Springer, Heidelberg (2001)

    Google Scholar 

  9. Fonseca, N.A., Silva, F., Camacho, R.: April - An Inductive Logic Programming System. In: Fisher, M., van der Hoek, W., Konev, B., Lisitsa, A. (eds.) JELIA 2006. LNCS (LNAI), vol. 4160, pp. 481–484. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  10. Soares, T., Ferreira, M., Rocha, R.: The MYDDAS Programmer’s Manual. Technical Report DCC-2005-10, Department of Computer Science, University of Porto (2005)

    Google Scholar 

  11. Shen, W.-M., Leng, B.: Metapattern Generation for Integrated Data Mining. In: Knowledge Discovery and Data Mining, pp. 152–157 (1996)

    Google Scholar 

  12. Brockhausen, P., Morik, K.: Direct Access of an ILP Algorithm to a Database Management System. In: MLnet Familiarization Workshop on Data Mining with Inductive Logic Programing, pp. 95–100 (1996)

    Google Scholar 

  13. Morik, K.: Knowledge Discovery in Databases - an Inductive Logic Programming Approach. In: Foundations of Computer Science: Potential - Theory - Cognition, pp. 429–436. Springer, Heidelberg (1997)

    Google Scholar 

  14. Bockhorst, J., Ong, I.M.: FOIL-D: Efficiently Scaling FOIL for Multi-Relational Data Mining of Large Datasets. In: Camacho, R., King, R., Srinivasan, A. (eds.) ILP 2004. LNCS (LNAI), vol. 3194, pp. 63–79. Springer, Heidelberg (2004)

    Google Scholar 

  15. Botta, M., Giordana, A., Saitta, L., Sebag, M.: Relational Learning as Search in a Critical Region. Journal of Machine Learning Research 4, 431–463 (2003)

    Article  MathSciNet  Google Scholar 

  16. Codd, E.F.: A relational model for large shared data banks. Communications of the ACM 13(6), 377–387 (1970)

    Article  MATH  Google Scholar 

  17. Ullman, J.D.: Principles of Database and Knowledge-Base Systems. Computer Science Press (1989)

    Google Scholar 

  18. Muggleton, S., Raedt, L.D.: Inductive Logic Programming: Theory and Methods. Journal of Logic Programming 19/20, 629–679 (1994)

    Article  Google Scholar 

  19. Soares, T., Rocha, R., Ferreira, M.: Generic Cut Actions for External Prolog Predicates. In: Van Hentenryck, P. (ed.) PADL 2006. LNCS, vol. 3819, pp. 16–30. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  20. Blockeel, H., Dehaspe, L., Demoen, B., Janssens, G., Ramon, J., Vandecasteele, H.: Improving the Efficiency of Inductive Logic Programming Through the Use of Query Packs. Journal of Machine Learning Research 16, 135–166 (2002)

    MATH  Google Scholar 

  21. Muggleton, S.: Inverse Entailment and Progol. New Generation Computing, Special Issue on Inductive Logic Programming 13, 245–286 (1995)

    Google Scholar 

  22. Blockeel, H., Raedt, L.D.: Top-Down Induction of First-Order Logical Decision Trees. Artificial Intelligence 101, 285–297 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  23. McCreath, E., Sharma, A.: Extraction of meta-knowledge to restrict the hypothesis space for ILP systems. In: Australian Joint Conference on Artificial Intelligence, pp. 75–82. World Scientific, Singapore (1995)

    Google Scholar 

  24. Santos Costa, V., Srinivasan, A., Camacho, R., Blockeel, H., Demoen, B., Janssens, G., Struyf, J., Vandecasteele, H., Laer, W.V.: Query Transformations for Improving the Efficiency of ILP Systems. Journal of Machine Learning Research 4, 465–491 (2002)

    Article  Google Scholar 

  25. Srinivasan, A.: A study of two sampling methods for analysing large datasets with ILP. Data Mining and Knowledge Discovery 3(1), 95–123 (1999)

    Article  Google Scholar 

  26. DiMaio, F., Shavlik, J.W.: Learning an Approximation to Inductive Logic Programming Clause Evaluation. In: Camacho, R., King, R., Srinivasan, A. (eds.) ILP 2004. LNCS (LNAI), vol. 3194, pp. 80–97. Springer, Heidelberg (2004)

    Google Scholar 

  27. Berardi, M., Varlaro, A., Malerba, D.: On the Effect of Caching in Recursive Theory Learning. In: Camacho, R., King, R., Srinivasan, A. (eds.) ILP 2004. LNCS (LNAI), vol. 3194, pp. 44–62. Springer, Heidelberg (2004)

    Google Scholar 

  28. Rocha, R., Fonseca, N.A., Santos Costa, V.: On Applying Tabling to Inductive Logic Programming. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 707–714. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  29. Fonseca, N.A., Silva, F., Camacho, R.: Strategies to Parallelize ILP Systems. In: Kramer, S., Pfahringer, B. (eds.) ILP 2005. LNCS (LNAI), vol. 3625, pp. 136–153. Springer, Heidelberg (2005)

    Google Scholar 

  30. Weber, I.: Discovery of First-Order Regularities in a Relational Database Using Offline Candidate Determination. In: Džeroski, S., Lavrač, N. (eds.) Inductive Logic Programming. LNCS, vol. 1297, pp. 288–295. Springer, Heidelberg (1997)

    Google Scholar 

  31. Dehaspe, L., Toironen, H.: Discovery of Relational Association Rules. In: Relational Data Mining, pp. 189–208. Springer, Heidelberg (2000)

    Google Scholar 

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Authors and Affiliations

  1. DCC-FC & LIACC, University of Porto, Portugal

    Michel Ferreira, Nuno A. Fonseca, Ricardo Rocha & Tiago Soares

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  1. Michel Ferreira
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  2. Nuno A. Fonseca
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  3. Ricardo Rocha
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  4. Tiago Soares
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Stephen Muggleton Ramon Otero Alireza Tamaddoni-Nezhad

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Ferreira, M., Fonseca, N.A., Rocha, R., Soares, T. (2007). Efficient and Scalable Induction of Logic Programs Using a Deductive Database System. In: Muggleton, S., Otero, R., Tamaddoni-Nezhad, A. (eds) Inductive Logic Programming. ILP 2006. Lecture Notes in Computer Science(), vol 4455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73847-3_22

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  • DOI: https://doi.org/10.1007/978-3-540-73847-3_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73846-6

  • Online ISBN: 978-3-540-73847-3

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