Abstract
The Semantic Web needs methodologies to accomplish actual commitment on shared ontologies among different actors in play. In this paper, we propose a machine learning approach to solve this issue relying on classified instance exchange and inductive reasoning. This approach is based on the idea that, whenever two (or more) software entities need to align their ontologies (which amounts, from the point of view of each entity, to add one or more new concept definitions to its own ontology), it is possible to learn the new concept definitions starting from shared individuals (i.e. individuals already described in terms of both ontologies, for which the entities have statements about classes and related properties); these individuals, arranged in two sets of positive and negative examples for the target definition, are used to solve a learning problem which as solution gives the definition of the target concept in terms of the ontology used for the learning process. The method has been applied in a preliminary prototype for a small multi-agent scenario (where the two entities cited before are instantiated as two software agents). Following the prototype presentation, we report on the experimental results we obtained and then draw some conclusions.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11914853_71.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Scientific American (2001)
Gruber, T.R.: A translation approach to portable ontology specifications (1993)
Noy, N.F.: Tools for mapping and merging ontologies. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies. International Handbooks on Information Systems, pp. 365–384. Springer, Heidelberg (2004)
Doan, A., Madhavan, J., Domingos, P., Halevy, A.Y.: Learning to map between ontologies on the semantic web. In: WWW, pp. 662–673 (2002)
Doan, A., Domingos, P., Halevy, A.Y.: Learning to match the schemas of data sources: A multistrategy approach. Machine Learning 50, 279–301 (2003)
Natalya, F., Noy, M.A.M.: The prompt suite: interactive tools for ontology merging and mapping. International Journal of Human-Computer Studies 59, 983–1024 (2003)
Ehrig, M., Staab, S., Sure, Y.: Bootstrapping ontology alignment methods with APFEL. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 186–200. Springer, Heidelberg (2005)
Mitchell, T.M.: Concept Learning and General to Specific Ordering. In: Machine Learning, pp. 20–51. McGraw-Hill, New York (1997)
dAmato, C., Fanizzi, N., Esposito, F.: A semantic similarity measure for expressive description logics. In: Proceedings of CILC 2005 (2005)
Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P. (eds.): The Description Logic Handbook. Cambridge University Press, Cambridge (2003)
van der Laag, P.R.J., Nienhuys-Cheng, S.H.: Existence and nonexistence of complete refinement operators. In: Bergadano, F., De Raedt, L. (eds.) ECML 1994. LNCS, vol. 784, pp. 307–322. Springer, Heidelberg (1994)
Nienhuys-Cheng, S., de Wolf, R.: Foundations of Inductive Logic Programming. LNCS (LNAI), vol. 1228. Springer, Heidelberg (1997)
Badea, L., Nienhuys-Cheng, S.-H.: A refinement operator for description logics. In: Cussens, J., Frisch, A.M. (eds.) ILP 2000. LNCS, vol. 1866, pp. 40–59. Springer, Heidelberg (2000)
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)
Vere, S.: Multilevel counterfactuals for generalizations of relational concepts and productions. Artificial Intelligence 14, 139–164 (1980)
Teege, G.: A subtraction operation for description logics. In: Torasso, P., Doyle, J., Sandewall, E. (eds.) Proceedings of the 4th International Conference on Principles of Knowledge Representation and Reasoning, pp. 540–550. Morgan Kaufmann, San Francisco (1994)
Winston, P.: Learning Structural Descriptions from Examples. Ph.D. dissertation. MIT, Cambridge (1970)
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
Palmisano, I., Iannone, L., Redavid, D., Semeraro, G. (2006). Towards an Inductive Methodology for Ontology Alignment Through Instance Negotiation. In: Meersman, R., Tari, Z. (eds) On the Move to Meaningful Internet Systems 2006: CoopIS, DOA, GADA, and ODBASE. OTM 2006. Lecture Notes in Computer Science, vol 4275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11914853_68
Download citation
DOI: https://doi.org/10.1007/11914853_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-48287-1
Online ISBN: 978-3-540-48289-5
eBook Packages: Computer ScienceComputer Science (R0)