This paper presents a new measure of semantic similarity in an is-a taxonomy, based on the notion of information content. Experimental evaluation suggests that the measure performs encouragingly well (a correlation of r = 0.79 with a benchmark set of human similarity judgments, with an upper bound of r = 0.90 for human subjects performing the same task), and significantly better than the traditional edge counting approach (r = 0.66).
A measure of similarity based on the concept of information content (IC) was presented by Resnik REF .
Using Information Content to Evaluate Semantic Similarity in a Taxonomy
Proceedings of the 14th International Joint Conference on Artificial Intelligence
Computer Science