Abstract
This work investigates on kernels that are applicable to semantic annotations expressed in Description Logics which are the theoretical counterpart of the standard representations for the Semantic Web. Namely, the focus is on the definition of a kernel for the \(\mathcal{ALC}\) logic, based both on the syntax and on the semantics of concept descriptions. The kernel is proved to be valid. Furthermore, semantic distance measures are induced from the kernel function.
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
Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Scientific American 284, 34–43 (2001)
Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P. (eds.): The Description Logic Handbook. Cambridge University Press, Cambridge (2003)
Cumby, C.M., Roth, D.: Learning with feature description logics. In: Matwin, S., Sammut, C. (eds.) ILP 2002. LNCS (LNAI), vol. 2583, pp. 32–47. Springer, Heidelberg (2003)
Schölkopf, B., Smola, A.J.: Learning with Kernels. MIT Press, Cambridge (2002)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)
Gärtner, T., Lloyd, J., Flach, P.: Kernels and distances for structured data. Machine Learning 57, 205–232 (2004)
Passerini, A., Frasconi, P., Raedt, L.D.: Kernels on prolog proof trees: Statistical learning in the ILP setting. Journal of Machine Learning Research 7, 307–342 (2006)
Haussler, D.: Convolution kernels on discrete structures. Technical Report UCSC-CRL-99-10, Department of Computer Science, University of California – Santa Cruz (1999)
Khardon, R., Roth, D., Servedio, R.: Efficiency versus convergence of boolean kernels for on-line learning algorithms. MIT Press, Cambridge (2002)
Gärtner, T.: A survey of kernels for structured data. SIGKDD Explorations 5, 49–58 (2003)
Cumby, C., Roth, D.: On kernel methods for relational learning. In: Fawcett, T., Mishra, N. (eds.) Proceedings of the 20th International Conference on Machine Learning, ICML 2003, pp. 107–114. AAAI Press, Menlo Park (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fanizzi, N., d’Amato, C. (2006). A Declarative Kernel for \(\mathcal{ALC}\) Concept Descriptions. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds) Foundations of Intelligent Systems. ISMIS 2006. Lecture Notes in Computer Science(), vol 4203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875604_37
Download citation
DOI: https://doi.org/10.1007/11875604_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45764-0
Online ISBN: 978-3-540-45766-4
eBook Packages: Computer ScienceComputer Science (R0)