Ontology merging and alignment is one of the effective methods for ontology sharing and reuse on the Semantic Web. A number of ontology merging and alignment tools have been developed, many of those tools depend mainly on concept (dis)similarity measure derived from linguistic cues. We present in this paper a linguistic information based approach to ontology merging and alignment. Our approach is based on two observations: majority of concept names used in ontology are composed of multiple-word combinations, and ontologies designed independently are, in most cases, organized in very different hierarchical structure even though they describe overlapping domains. These observations led us to a merging and alignment algorithm that utilizes both the local and global meaning of a concept. We devised our proposed algorithm in Mo A, an OWL DL ontology merging and alignment tool. We tested MoA on 3 ontology pairs, and human experts followed 93% of the MoA's suggestions. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Kim, J., Jang, M., Ha, Y. G., Sohn, J. C., & Lee, S. J. (2005). MoA: OWL ontology merging and alignment tool for the Semantic Web. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3533 LNAI, pp. 722–731). Springer Verlag. https://doi.org/10.1007/11504894_100
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