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A Counting-Based Heuristic for ILP-Based Concept Discovery Systems

  • Alev Mutlu
  • Pınar Karagoz
  • Yusuf Kavurucu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8073)

Abstract

Concept discovery systems are concerned with learning definitions of a specific relation in terms of other relations provided as background knowledge. Although such systems have a history of more than 20 years and successful applications in various domains, they are still vulnerable to scalability and efficiency issues —mainly due to large search spaces they build. In this study we propose a heuristic to select a target instance that will lead to smaller search space without sacrificing the accuracy. The proposed heuristic is based on counting the occurrences of constants in the target relation. To evaluate the heuristic, it is implemented as an extension to the concept discovery system called C 2 D. The experimental results show that the modified version of C 2 D builds smaller search space and performs better in terms of running time without any decrease in coverage in comparison to the one without extension.

Keywords

Inductive Logic Programming Concept Discovery Search Space Counting 

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References

  1. 1.
    Muggleton, S.: Inductive Logic Programming. In: The MIT Encyclopedia of the Cognitive Sciences (MITECS). MIT Press (1999)Google Scholar
  2. 2.
    Dzeroski, S.: Multi-relational data mining: An introduction. SIGKDD Explorations 5(1), 1–16 (2003)CrossRefGoogle Scholar
  3. 3.
    Muggleton, S., Feng, C.: Efficient induction of logic programs. In: Proceedings of the 1st Conference on Algorithmic Learning Theory, pp. 368–381. Springer/Ohmsma (1990)Google Scholar
  4. 4.
    Zelezny, F., Srinivasan, A., David Page Jr., C.: Randomised restarted search in ilp. Machine Learning 64(1-3), 183–208 (2006)zbMATHCrossRefGoogle Scholar
  5. 5.
    Serrurier, M., Prade, H.: Improving inductive logic programming by using simulated annealing. Information Sciences 178(6), 1423–1441 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Kavurucu, Y., Senkul, P., Toroslu, I.H.: Ilp-based concept discovery in multi-relational data mining. Expert Syst. Appl. 36(9), 11418–11428 (2009)CrossRefGoogle Scholar
  7. 7.
    Nassif, H., Page, D., Ayvaci, M., Shavlik, J., Burnside, E.S.: Uncovering age-specific invasive and dcis breast cancer rules using inductive logic programming. In: Proceedings of the 1st ACM International Health Informatics Symposium, pp. 76–82. ACM (2010)Google Scholar
  8. 8.
    Nassif, H., Al-Ali, H., Khuri, S., Keirouz, W., Page, D.: An inductive logic programming approach to validate hexose binding biochemical knowledge. In: De Raedt, L. (ed.) ILP 2009. LNCS, vol. 5989, pp. 149–165. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Amini, A., Shrimpton, P.J., Muggleton, S.H., Sternberg, M.J.: A general approach for developing system-specific functions to score protein–ligand docked complexes using support vector inductive logic programming. Proteins: Structure, Function, and Bioinformatics 69(4), 823–831 (2007)CrossRefGoogle Scholar
  10. 10.
    Fonseca, N.A., Pereira, M., Santos Costa, V., Camacho, R.: Interactive discriminative mining of chemical fragments. In: Frasconi, P., Lisi, F.A. (eds.) ILP 2010. LNCS, vol. 6489, pp. 59–66. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Konstantopoulos, S.: A data-parallel version of Aleph. CoRR abs/0708.1527 (2007)Google Scholar
  12. 12.
    Mutlu, A., Senkul, P.: Improving hit ratio of ilp-based concept discovery system with memoization. The Computer Journal (2012), doi:10.1093/comjnl/bxs163Google Scholar
  13. 13.
    Blockeel, H., Dehaspe, L., Demoen, B., Janssens, G., Vandecasteele, H.: Improving the efficiency of inductive logic programming through the use of query packs. Journal of Artificial Intelligence Research 16, 135–166 (2002)zbMATHGoogle Scholar
  14. 14.
    Tausend, B.: Representing biases for inductive logic programming. In: Proceedings of the 7th European Conference on Machine Learning, Catania, Italy, April 6-8, pp. 427–430 (1994)Google Scholar
  15. 15.
    Kavurucu, Y., Senkul, P., Toroslu, I.H.: Concept discovery on relational databases: New techniques for search space pruning and rule quality improvement. Knowl.-Based Syst. 23(8), 743–756 (2010)CrossRefGoogle Scholar
  16. 16.
    Srinivasan, A.: The Aleph Manual (1999), http://www.comlab.ox.ac.uk/activities/machinelearning/Aleph/ (accessed April 06, 2013)
  17. 17.
  18. 18.
    Dolšak, B., Bratko, I., Jezernik, A.: Finite element mesh design: An engineering domain for ILP application. In: Proceedings of the 4th International Workshop on Inductive Logic Programming, Bonn, Germany, Gesellschaft für Mathematik und Datenverarbeitung MBH, September 12-14, pp. 305–320 (1994)Google Scholar
  19. 19.
    Srinivasan, A., King, R.D., Muggleton, S.H., Sternberg, M.: The predictive toxicology evaluation challenge. In: IJCAI 1997: Proceedings of the 15th International Joint Conference on Artificial Intelligence, pp. 1–6 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alev Mutlu
    • 1
  • Pınar Karagoz
    • 1
  • Yusuf Kavurucu
    • 2
  1. 1.Department of Computer EngineeringMiddle East Technical UniversityAnkaraTurkey
  2. 2.Turkish Naval AcademyTurkey

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