Abstract
We compare our graph-based relational concept learning approach “SubdueCL” with the ILP systems FOIL and Progol. In order to be fair in the comparison, we use the conceptual graphs representation. Conceptual graphs have a standard translation from graphs into logic. In this way, we introduce less bias during the translation process. We experiment with different types of domains. First, we show our experiments with an artificial domain to describe how SubdueCL performs with the conceptual graphs representation. Second, we experiment with several flat and relational domains. The results of the comparison show that the SubdueCL system is competitive with ILP systems in both flat and relational domains.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Cameron-Jones, R. M., and J. R. Quinlan. (1994). Efficient Top-down Induction of Logic Programs. SIGART Bulletin, 5, 1:33–42.
Cook, D. J., and L. B. Holder. (1994). Substructure Discovery Using Minimum Description Length and Background Knowledge. Journal of Artificial Intelligence Research. 1:231–55.
Cook, D. J., and L. B. Holder. (2000). Graph-Based Data Mining. IEEE Intelligent Systems, 15(2):32–41.
Gonzalez, Jesus A. (2001). Empirical and Theoretical Análisis of Relational Concept Learning Using a Graph-Based Representation. Doctoral dissertation, Department of Computer Science, University of Texas at Arlington.
Gonzalez, Jesus A., L. B. Holder, and Diane J. Cook. (2001). Application of Graph-Based Concept Learning to the Predictive Toxicology Domain. Proceedings of the Predictive Toxicology Challenge Workshop.
Keogh, E., C. Blake, and C. J. Merz. (1998). In UCI Repository of Machine Learning Databases.
Lehmann, F. (1992). Semantics Networks in Artificial Intelligence. Oxford: Pergamon Press.
Muggleton S. (1995). Inverse Entailment and Progol. New Generation Computing, 13:245–86.
Muggleton, S. and C. Feng. (1992). Efficient Induction of Logic Programs. Inductive Logic Programming, Academic Press 281–97.
Rissanen, J. (1989). Stochastic Complexity in Statistical Inquiry. World Scientific Publishing Company.
Sowa, J. F. (1984). Conceptual Structures: Information Processing in Mind and Machine. Addison-Wesley.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gonzalez, J.A., Holder, L.B., Cook, D.J. (2003). Experimental Comparison of Graph-Based Relational Concept Learning with Inductive Logic Programming Systems. In: Matwin, S., Sammut, C. (eds) Inductive Logic Programming. ILP 2002. Lecture Notes in Computer Science(), vol 2583. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36468-4_6
Download citation
DOI: https://doi.org/10.1007/3-540-36468-4_6
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00567-4
Online ISBN: 978-3-540-36468-9
eBook Packages: Springer Book Archive