On the LearnAbility of Abstraction Theories from Observations for Relational Learning

  • Stefano Ferilli
  • Teresa M. A. Basile
  • Nicola Di Mauro
  • Floriana Esposito
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3720)


The most common methodology in symbolic learning consists in inducing, given a set of observations, a general concept definition. It is widely known that the choice of the proper description language for a learning problem can affect the efficacy and effectiveness of the learning task. Furthermore, most real-world domains are affected by various kinds of imperfections in data, such as inappropriateness of the description language which does not contain/facilitate an exact representation of the target concept. To deal with such kind of situations, Machine Learning approaches moved from a framework exploiting a single inference mechanism, such as induction, towards one integrating multiple inference strategies such as abstraction. The literature so far assumed that the information needed to the learning systems to apply additional inference strategies is provided by a domain expert. The goal of this work is the automatic inference of such information.

The effectiveness of the proposed method was tested by providing the generated abstraction theories to the learning system INTHELEX as a background knowledge to exploit its abstraction capabilities. Various experiments were carried out on the real-world application domain of scientific paper documents, showing the validity of the approach.


Target Concept Inductive Logic Programming Inductive Learning Inference Strategy Unary Predicate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Ceri, S., Gottlöb, G., Tanca, L.: Logic Programming and Databases. Springer, Heidelberg (1990)Google Scholar
  2. 2.
    De Raedt, L.: Interactive Theory Revision - An Inductive Logic Programming Approach. Academic Press, London (1992)Google Scholar
  3. 3.
    Drastah, G., Czako, G., Raatz, S.: Induction in an abstraction space: A form of constructive induction. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 708–712 (1989)Google Scholar
  4. 4.
    Esposito, F., Ferilli, S., Fanizzi, N., Basile, T.M.A., Di Mauro, N.: Incremental multistrategy learning for document processing. Applied Artificial Intelligence: An Internationa Journal 17(8/9), 859–883 (2003)CrossRefGoogle Scholar
  5. 5.
    Ferilli, S., Di Mauro, N., Basile, T.M.A., Esposito, F.: Incremental induction of rules for document image understanding. In: Cappelli, A., Turini, F. (eds.) AI*IA 2003. LNCS, vol. 2829, pp. 176–188. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Flann, N.S., Dietterich, T.G.: Selecting appropriate representations for learning from examples. In: AAAI, pp. 460–466 (1986)Google Scholar
  7. 7.
    Giordana, A., Roverso, D., Saitta, L.: Abstracting concepts with inverse resolution. In: Proceedings of the 8th International Workshop on Machine Learning, Evanston, IL, pp. 142–146. Morgan Kaufmann, San Francisco (1991)Google Scholar
  8. 8.
    Giordana, A., Saitta, L.: Abstraction: A general framework for learning. In: Working Notes of the Workshop on Automated Generation of Approximations and Abstractions, Boston, MA, pp. 245–256 (1990)Google Scholar
  9. 9.
    Kanellakis, P.C.: Elements of relational database theory. In: Van Leeuwen, J. (ed.) Handbook of Theoretical Computer Science. Formal Models and Semantics, vol. B, pp. 1073–1156. Elsevier Science Publishers, Amsterdam (1990)Google Scholar
  10. 10.
    Muggleton, S.H., De Raedt, L.: Inductive logic programming. Journal of Logic Programming: Theory and Methods 19, 629–679 (1994)CrossRefGoogle Scholar
  11. 11.
    Rouveirol, C., Puget, J.: Beyond inversion of resolution. In: Proceedings of ICML 1997, Austin, TX, pp. 122–130. Morgan Kaufmann, San Francisco (1990)Google Scholar
  12. 12.
    Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing and Management 24(5), 513–523 (1988)CrossRefGoogle Scholar
  13. 13.
    Utgoff, P.E.: Shift of bias for inductive concept learning. In: Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (eds.) Machine Learning: an artificial intelligence approach, vol. II, pp. 107–148. Morgan Kaufmann, Los Altos (1986)Google Scholar
  14. 14.
    Zucker, J.-D.: Semantic abstraction for concept representation and learning. In: Michalski, R.S., Saitta, L. (eds.) Proceedings of the 4th International Workshop on Multistrategy Learning, pp. 157–164 (1998)Google Scholar
  15. 15.
    Zucker, J.-D.: A grounded theory of abstraction in artificial intelligence. Philosophical Transactions: Biological Sciences 358(1435), 1293–1309 (2003)CrossRefGoogle Scholar
  16. 16.
    Zucker, J.-D., Ganascia, J.-G.: Representation changes for efficient learning in structural domains. In: Saitta, L. (ed.) Proceedings of the 13th International Conference on Machine Learning, pp. 543–551. Morgan Kaufmann, San Francisco (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Stefano Ferilli
    • 1
  • Teresa M. A. Basile
    • 1
  • Nicola Di Mauro
    • 1
  • Floriana Esposito
    • 1
  1. 1.Department of Computer ScienceUniversity of BariItaly

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