Ontology matching is a main step for integrating overlapping domains of knowledge and establishing interoperation among semantic web application. As information sources grow rapidly, manual ontology matching becomes more tedious and time-consuming and consequently leads to errors and frustration. In this paper we developed the new lexical and semantic similarity measure by using the lexical database ConceptNet. The proposed strategy used new lexical and semantic matching for finding the correspondence entities. In the semantic approach we use the electronic lexical database, ConceptNet for identifying the similar entities and create similarity matrices according to that. We evaluate the proposed measure using standard methods of precision and recall, tested on a well- known benchmark and also compared to other algorithms presented in the paper. The experimental results show the proposed algorithm is effective and outperforms other algorithms.