A Multi-level Matching Algorithm for Combining Similarity Measures in Ontology Integration

Various similarity measures have been proposed for ontology integration to identify and suggest possible matches of components in a semi-automatic process. A (basic) Multi Match Algorithm (MMA) can be used to combine these measures effectively, thus making it easier for users in such applications to identify "ideal" matches found. We propose a multi-level extension of MMA, called MLMA, which assumes the collection of similarity measures are partitioned by the user, and that there is a partial order on the partitions, also defined by the user. We have developed a running prototype of the proposed multi level method and illustrate how our method yields improved match results compared to the basic MMA. While our objective in this study has been to develop tools and techniques to support the hybrid approach we introduced earlier for ontology integration, the ideas can be applied in information sharing and ontology integration applications.

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