This thesis mainly concerns itself with the issue of semantic similarity and computational applications of it. Semantic similarity has for a long time been a subject of intense scholarship in the fields of Artificial Intelligence, Psychol-ogy and Cognitive Science. Computational models trying to imitate aspects of this cognitive ability date back to Quillian and his spreading activation algorithm. In the research presented here we propose a new computational approach to the assessment of semantic similarity between concepts. This work builds upon previous information theoretic attempts, from researchers interested in natural language processing, to mimic this ill-defined human aptitude. Word-Net, an ontological lexical knowledge base, is the only source of knowledge exploited in the proposed algorithm. Hence, this is where our work differs