Abstract
In this paper we focus on learning concept descriptions expressed in Description Logics. After stating the learning problem in this context, a FOIL-like algorithm is presented that can be applied to general DL languages, discussing related theoretical aspects of learning with the inherent incompleteness underlying the semantics of this representation. Subsequently we present an experimental evaluation of the implementation of this algorithm performed on some real ontologies in order to empirically assess its performance.
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.
References
Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P. (eds.): The Description Logic Handbook. Cambridge University Press, Cambridge (2003)
Baader, F., Ganter, B., Sertkaya, B., Sattler, U.: Completing description logic knowledge bases using formal concept analysis. In: Veloso, M. (ed.) Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, pp. 230–235 (2007)
Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Scientific American 284(5), 34–43 (2001)
Borgida, A.: On the relative expressiveness of description logics and predicate logics. Artificial Intelligence 82(1–2), 353–367 (1996)
Brandt, S., Küsters, R., Turhan, A.-Y.: Approximation and difference in description logics. In: Fensel, D., Giunchiglia, F., McGuinness, D., Williams, M.-A. (eds.) Proceedings of the International Conference on Knowledge Representation, pp. 203–214. Morgan Kaufmann, San Francisco (2002)
Cohen, W.W., Hirsh, H.: Learnability of description logics. In: Proceedings of the Fourth Annual Workshop on Computational Learning Theory, Pittsburgh, PA. ACM Press, New York (1992)
Cohen, W.W., Hirsh, H.: Learning the CLASSIC description logic. In: Torasso, P., Doyle, J., Sandewall, E. (eds.) Proceedings of the 4th International Conference on the Principles of Knowledge Representation and Reasoning, pp. 121–133. Morgan Kaufmann, San Francisco (1994)
d’Amato, C., Fanizzi, N., Esposito, F.: Query answering and ontology population: An inductive approach. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 288–302. Springer, Heidelberg (2008)
Dean, M., Schreiber, G.: Web Ontology Language Reference. W3C recommendation, W3C (2004), http://www.w3.org/TR/owl-ref
Esposito, F., Fanizzi, N., Iannone, L., Palmisano, I., Semeraro, G.: Knowledge-intensive induction of terminologies from metadata. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 441–455. Springer, Heidelberg (2004)
Goldman, S.A., Kwek, S., Scott, S.D.: Learning from examples with unspecified attribute values. Information and Computation 180(2), 82–100 (2003)
Grosof, B.N., Horrocks, I., Volz, R., Decker, S.: Description logic programs: combining logic programs with description logic. In: Proceedings of the 12th international conference on World Wide Web, WWW 2003, pp. 48–57. ACM Press, New York (2003)
Iannone, L., Palmisano, I., Fanizzi, N.: An algorithm based on counterfactuals for concept learning in the semantic web. Applied Intelligence 26(2), 139–159 (2007)
Inuzuka, N., Kamo, M., Ishii, N., Seki, H., Itoh, H.: Tow-down induction of logic programs from incomplete samples. In: Muggleton, S. (ed.) ILP 1996. LNCS, vol. 1314, pp. 265–282. Springer, Heidelberg (1997)
Kietz, J.-U.: Learnability of description logic programs. In: Matwin, S., Sammut, C. (eds.) ILP 2002. LNCS (LNAI), vol. 2583, pp. 117–132. Springer, Heidelberg (2003)
Kietz, J.-U., Morik, K.: A polynomial approach to the constructive induction of structural knowledge. Machine Learning 14(2), 193–218 (1994)
Lehmann, J., Hitzler, P.: Foundations of refinement operators for description logics. In: Blockeel, H., Ramon, J., Shavlik, J., Tadepalli, P. (eds.) ILP 2007. LNCS (LNAI), vol. 4894, pp. 161–174. Springer, Heidelberg (2008)
Lehmann, J., Hitzler, P.: A refinement operator based learning algorithm for the \({\cal ALC}\) description logic. In: Blockeel, H., Ramon, J., Shavlik, J., Tadepalli, P. (eds.) ILP 2007. LNCS (LNAI), vol. 4894, pp. 147–160. Springer, Heidelberg (2008)
Lisi, F.A.: Principles of inductive reasoning on the Semantic Web: A framework for learning in \(\mathcal{AL}\)-Log. In: Fages, F., Soliman, S. (eds.) PPSWR 2005. LNCS, vol. 3703, pp. 118–132. Springer, Heidelberg (2005)
Quinlan, R.: Learning logical definitions from relations. Machine Learning 5, 239–266 (1990)
Rouveirol, C., Ventos, V.: Towards learning in CARIN-\(\mathcal{ALN}\). In: Cussens, J., Frisch, A.M. (eds.) ILP 2000. LNCS (LNAI), vol. 1866, pp. 191–208. Springer, Heidelberg (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fanizzi, N., d’Amato, C., Esposito, F. (2008). DL-FOIL Concept Learning in Description Logics. In: Železný, F., Lavrač, N. (eds) Inductive Logic Programming. ILP 2008. Lecture Notes in Computer Science(), vol 5194. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85928-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-540-85928-4_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85927-7
Online ISBN: 978-3-540-85928-4
eBook Packages: Computer ScienceComputer Science (R0)