Abstract
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 MoA, 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.
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Kim, J., Jang, M., Ha, YG., Sohn, JC., Lee, S.J. (2005). MoA: OWL Ontology Merging and Alignment Tool for the Semantic Web. In: Ali, M., Esposito, F. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2005. Lecture Notes in Computer Science(), vol 3533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11504894_100
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DOI: https://doi.org/10.1007/11504894_100
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26551-1
Online ISBN: 978-3-540-31893-4
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