  In recent years, semantic similarity measure has a great interest in Semantic
Web and Natural Language Processing (NLP). Several similarity measures have
been developed, being given the existence of a structured knowledge
representation offered by ontologies and corpus which enable semantic
interpretation of terms. Semantic similarity measures compute the similarity
between concepts/terms included in knowledge sources in order to perform
estimations. This paper discusses the existing semantic similarity methods
based on structure, information content and feature approaches. Additionally,
we present a critical evaluation of several categories of semantic similarity
approaches based on two standard benchmarks. The aim of this paper is to give
an efficient evaluation of all these measures which help researcher and
practitioners to select the measure that best fit for their requirements.
