An evidence based approach for multipe similarity measures combining for ontology mapping

Ontologies nowadays are known to be potential tool for enabling interoperability across heterogeneous systems and semantic web applications. For this, Ontology mapping is required for combining distributed and heterogeneous ontologies modeling the same domain in order to establish correspondences among the related entities of the ontologies sources. Many works dealing with ontology mapping have been proposed. The addressed contribution in this paper is the conflict resolution among the different similarity measures using the Dempster Shafer theory into the mapping. The proposed method consists on combining the similarities which were originally created by syntactic and structural similarity algorithms. The similarity techniques represent the elementary masses to reason on and the management of derived similarity measures will be based on fusion theory to model uncertainty and treat ambiguity to resolve conflicts between the different elementary sources. The generated data will be interpreted in terms of logic correspondence between the entities of interest.