Towards an Inductive Methodology for Ontology Alignment Through Instance Negotiation
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.
KeywordsDescription Logic Concept Learn Inductive Logic Programming Ontology Alignment Ontological Primitive
Unable to display preview. Download preview PDF.
- 1.Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Scientific American (2001)Google Scholar
- 2.Gruber, T.R.: A translation approach to portable ontology specifications (1993)Google Scholar
- 3.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)Google Scholar
- 4.Doan, A., Madhavan, J., Domingos, P., Halevy, A.Y.: Learning to map between ontologies on the semantic web. In: WWW, pp. 662–673 (2002)Google Scholar
- 8.Mitchell, T.M.: Concept Learning and General to Specific Ordering. In: Machine Learning, pp. 20–51. McGraw-Hill, New York (1997)Google Scholar
- 9.dAmato, C., Fanizzi, N., Esposito, F.: A semantic similarity measure for expressive description logics. In: Proceedings of CILC 2005 (2005)Google Scholar
- 11.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)Google Scholar
- 12.Nienhuys-Cheng, S., de Wolf, R.: Foundations of Inductive Logic Programming. LNCS (LNAI), vol. 1228. Springer, Heidelberg (1997)Google Scholar
- 16.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)Google Scholar
- 17.Winston, P.: Learning Structural Descriptions from Examples. Ph.D. dissertation. MIT, Cambridge (1970)Google Scholar