Extracting semantic features of text in natural language processing activities are important for many applications. Measuring semantic similarity of text can be carried out by various methods. Given two concepts or two short texts, the similarity between them can be carried out by similarity measures like corpus based and knowledge based measures. Measures which are corpus based are application specific and this paper focuses on measuring semantic similarity using knowledge based measures. Existing knowledge based measures use either information content or path length between the concepts to evaluate the similarity. Hence, in this paper an approach which uses both information content and path length is designed to evaluate the similarity between the concepts and a thorough analysis is done on the benchmark datasets and with results it is shown that the proposed measure is more efficient than all the existing measures.