Schema Evolution publication categorizer

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.