Capturing Semantics Towards Automatic Coordination of Domain Ontologies
Existing efforts on ontology mapping, alignment and merging vary from methodological and theoretical frameworks, to methods and tools that support the semi-automatic coordination of ontologies. However, only latest research efforts “touch” on the mapping /merging of ontologies using the whole breadth of available knowledge. Addressing this issue, the work presented in this paper is based on the HCONE-merge approach that makes use of the intended informal interpretations of concepts by mapping them to WordNet senses using lexical semantic indexing (LSI). Our aim is to explore the level of human involvement required for mapping concepts of the source ontologies to their intended interpretations. We propose a series of methods for ontology mapping/merging with varying degrees of human involvement and evaluate them experimentally. We conclude that, although an effective fully automated process is not attainable, we can reach a point where the process of ontology mapping/merging can be carried out efficiently with minimum human involvement.
KeywordsOntology mapping Ontology merging Latent Semantic Indexing
Unable to display preview. Download preview PDF.
- 1.Uschold, M., Gruninger, M.: Creating Semantically Integrated Communities on the World Wide Web. In: Semantic Web Workshop, WWW 2002 Conference (May 2002) (invited talk)Google Scholar
- 2.Uschold, M.: Where are the Semantics in the Semantic Web? AI Magazine 24(3) (2003) (fall)Google Scholar
- 3.Madhavan, J., Bern-stein, P.A., Domingos, P., Halevy, A.Y.: Representing and reasoning about mappings between domain models. In: Proc. of the 18th AAAI, pp. 80–86 (2002)Google Scholar
- 7.Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by Latent Semantic Analysis. Journal of the American Society of Information Science (1990)Google Scholar
- 8.Madhavan, J., Bernstein, P.A., Rahm, E.: Generic schema matching with Cupid. VLDB Journal, 49–58 (2001)Google Scholar
- 9.Serafini, L., Bouquet, P., Magnini, B., Zanobini, S.: An Algorithm for Matching Contextualized Schemas via SAT. In: Proc.of CONTEX (2003)Google Scholar
- 10.Gangemi, A., Pisanelli, D.M., Steve, G.: An Overview of the ONIONS Project: Applying Ontologies to the Integration of Medical Terminologies. In: Data and Knowledge Engineering, vol. 31, pp. 183–220 (1999)Google Scholar
- 11.Stumme, G., Mädche, A.: FCA-Merge: Bottom-Up Merging of Ontologies. In: Nebel, B. (ed.) Proc. 17th Intl. Conf. on Artificial Intelligence (IJCAI 2001), Seattle, pp. 225–230 (2001)Google Scholar
- 12.Doan, A., Madhavan, J., Domingos, P., Halvey, A.: Learning to map between ontologies on the semantic web. In: Proc. Of WWW 2002, 11th InternationalWWW Conf., Hawaii (2002)Google Scholar
- 13.Noy, N., Musen, M.A.M.: PROMPT: Algorithm and tool for automated ontology merging and alignment. In: Proceedings of 7th National Conference on AI, Austin (2000)Google Scholar