Abstract
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.
Chapter PDF
References
Ceri, S., Gottlöb, G., Tanca, L.: Logic Programming and Databases. Springer, Heidelberg (1990)
De Raedt, L.: Interactive Theory Revision - An Inductive Logic Programming Approach. Academic Press, London (1992)
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)
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)
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)
Flann, N.S., Dietterich, T.G.: Selecting appropriate representations for learning from examples. In: AAAI, pp. 460–466 (1986)
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)
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)
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)
Muggleton, S.H., De Raedt, L.: Inductive logic programming. Journal of Logic Programming: Theory and Methods 19, 629–679 (1994)
Rouveirol, C., Puget, J.: Beyond inversion of resolution. In: Proceedings of ICML 1997, Austin, TX, pp. 122–130. Morgan Kaufmann, San Francisco (1990)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing and Management 24(5), 513–523 (1988)
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)
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)
Zucker, J.-D.: A grounded theory of abstraction in artificial intelligence. Philosophical Transactions: Biological Sciences 358(1435), 1293–1309 (2003)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ferilli, S., Basile, T.M.A., Di Mauro, N., Esposito, F. (2005). On the LearnAbility of Abstraction Theories from Observations for Relational Learning. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds) Machine Learning: ECML 2005. ECML 2005. Lecture Notes in Computer Science(), vol 3720. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564096_16
Download citation
DOI: https://doi.org/10.1007/11564096_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29243-2
Online ISBN: 978-3-540-31692-3
eBook Packages: Computer ScienceComputer Science (R0)