Assessment of semantic similarity between concepts is of great importance in many applications dealing with textual data, such as natural language processing, word sense disambiguation, knowledge acquisition, document semantic annotation and systems devoted to intelligent information retrieval. Moreover, to extract similar concepts from multiple ontologies, there is a real need to develop a conceptual similarity measure, the intention of finding semantic similarity among concepts in a given hierarchy, is to enhance the integration and retrieval of heterogeneous information resources in a more meaningful and accurate way. Recently, different measures have been proposed to evaluate semantic relatedness between two lexically expressed concepts, in particular the semantic similarity which is a special case of semantic relatedness. In this paper we present a hybrid approach for measuring the semantic similarity in a hierarchy. The aim of the work is to address a new issue that focuses on the sensitivity of the similarity measure between concepts in a hierarchy. Based both on the distance between the compared concepts through their Least Common Subsumer (LCS) and their information content, the measure is expected to reach a fine granularity level and provides more consistent and accurate measures. A case study is done using the universal WordNet taxonomy. The information content is set by the number of the hyponyms. Mainly the approach uses a hyponym logarithmic function; due to its intrinsic properties which may significantly enhance the precision and the distribution of the measure through the nodes in the hierarchy.