Abstract
Horn clause Logic is a powerful representation language exploited in Logic Programming as a computer programming framework and in Inductive Logic Programming as a formalism for expressing examples and learned theories in domains where relations among objects must be expressed to fully capture the relevant information. While the predicates that make up the description language are defined by the knowledge engineer and handled only syntactically by the interpreters, they sometimes express information that can be properly exploited only with reference to a suitable background knowledge in order to capture unexpressed and underlying relationships among the concepts described. This is typical when the representation includes numerical information, such as single values or intervals, for which simple syntactic matching is not sufficient.
This work proposes an extension of an existing framework for similarity assessment between First-Order Logic Horn clauses, that is able to handle numeric information in the descriptions.
The viability of the solution is demonstrated on sample problems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Biba, M., Esposito, F., Ferilli, S., Di Mauro, N., Basile, T.M.A.: Unsupervised discretization using kernel density estimation. In: IJCAI 2007, pp. 696–701 (2007)
Ceri, S., Gottlöb, G., Tanca, L.: Logic Programming and Databases. Springer, Heidelberg (1990)
Esposito, F., Fanizzi, N., Ferilli, S., Semeraro, G.: A generalization model based on oi-implication for ideal theory refinement. Fundamenta Informaticæ 47(1-2), 15–33 (2001)
Ferilli, S., Basile, T.M.A., Biba, M., Di Mauro, N., Esposito, F.: A general similarity framework for horn clause logic. Fundamenta Informaticæ 90(1-2), 43–46 (2009)
Ferilli, S., Biba, M., Mauro, N., Basile, T.M., Esposito, F.: Plugging taxonomic similarity in first-order logic horn clauses comparison. In: Serra, R., Cucchiara, R. (eds.) AI*IA 2009. LNCS, vol. 5883, pp. 131–140. Springer, Heidelberg (2009)
Lin, D.: An information-theoretic definition of similarity. In: Proc. 15th International Conf. on Machine Learning, pp. 296–304. Morgan Kaufmann, San Francisco (1998)
Lloyd, J.W.: Foundations of Logic Programming, 2nd edn. Springer, Heidelberg (1987)
Muggleton, S.: Inductive logic programming. New Generation Computing 8(4), 295–318 (1991)
Rouveirol, C.: Extensions of inversion of resolution applied to theory completion. In: Inductive Logic Programming, pp. 64–90. Academic Press, London (1992)
Srinivasan, A., Muggleton, S., King, R., Sternberg, M.: Mutagenesis: ILP experiments in a non-determinate biological domain. In: Wrobel, S. (ed.) Proceedings of the 4th International Workshop on Inductive Logic Programming. GMD-Studien, vol. 237, pp. 217–232 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ferilli, S., Basile, T.M.A., Di Mauro, N., Esposito, F. (2011). Plugging Numeric Similarity in First-Order Logic Horn Clauses Comparison. In: Pirrone, R., Sorbello, F. (eds) AI*IA 2011: Artificial Intelligence Around Man and Beyond. AI*IA 2011. Lecture Notes in Computer Science(), vol 6934. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23954-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-23954-0_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23953-3
Online ISBN: 978-3-642-23954-0
eBook Packages: Computer ScienceComputer Science (R0)