Due to the high heterogeneity of ontologies, a combination of many methods is necessary in order to discover correctly the semantic correspondences between their elements. An ontology matching tool can be seen as a collection of several matching components, each implementing a specific method dealing with a specific heterogeneity type (terminological, structural or semantic). In addition, a mapping selection module is introduced to filter out the most likely mapping candidates. This paper proposes an empirical study of the interaction between these components working together inside an ontology matching system. By the help of datasets from the Ontology Alignment Evaluation Initiative, we have carried out several experimental studies. In the first place, we have been interested in the impact of the mapping selection module on the performance of terminological and structural matchers revealing the advantage of using global methods vs. local ones. Further, we have carried an extensive study on the flaw of the performance of a structural matcher in the presence of noisy input coming from a terminological method. Finally, we have analyzed the behavior of a structural and a semantic component with respect to inputs taken from different terminological matchers.

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We propose a novel method to compute similarity between cross-ontology concepts based on the amount of overlap of the information content of their labels. We extend Tversky's similarity measure by using the information content of each term within an ontology label both for the similarity computation and for the weight assignment to tokens. The approach is suitable for handling compound labels. Our experiments showed that it outperforms existing terminological similarity measures for the ontology matching task.