Sentence similarity can be calculated by various measures, but the measures that can use semantic information between the words perform better compared to others. Out of these, the measures which are related to a corpus, or which are knowledge related are more significant in the sentence similarity domains. The applications which use knowledge based measures tend to give more accurate results and are very much in coincidence with human similarity. These measures use path length between the concepts or information content between the words to derive the similarity between the words. Some of the semantic similarity measures generate synonym sets of the words to evaluate similarity, but these measures focus mainly on generating noun or verb synonym sets which can be enhanced by generating all the synonym sets. In this paper, a metric PathM is proposed to calculate word-pair similarity by generating all the synonym sets of the words and it is enhanced to calculate the sentence similarity. The measure is compared with path measure and the results obtained are better in comparison with other measures.