OMeGA: Ontology matching enhanced by genetic algorithm

In this paper, we propose a new ontology matching approach, OMeGA, based on genetic algorithms applied on the graph structure of ontologies. Our approach finds the linguistic-structural similarities between concepts in two ontologies. It introduces new fitness functions and new criteria for categorizing test cases into four categories. Our approach does not need any extra information or resource with exception to the ontology itself. Experimental results on applying OMeGA on defined cases show higher performance compared to existing method.