Humans are able to easily judge if a pair of concepts are related in some way. Understanding of how humans are able to perform this task is not easy. Semantic similarity denotes computing the similarity between concepts, having the same meaning or related information, which are not necessarily lexically similar. Semantic similarity between concepts plays an important role in Semantic Web, knowledge sharing, Web mining, semantic sense understanding and text summarization. This also is an important problem in Natural Language Processing and Information Retrieval Researches. These techniques are becoming important components of most of the Information Retrieval (IR), Information Extraction (IE) and other intelligent knowledge based systems. Therefore it has received considerable attention in the literature. Ontology has a good hierarchical structure of concepts. In the ontology, semantic information can be realized through the semantic relationship of concepts. Ontology-based semantic similarity techniques can estimate the semantic similarity between two hierarchically expressed concepts in a given ontology or taxonomy. Semantic similarity is usually computed by mapping concepts to ontology and by examining their relationships in it. The most popular semantic similarity methods are implemented and evaluated using WordNet and MeSH. Several algorithmic approaches for computing semantic similarity have been proposed. This paper discusses the various approaches used for identifying semantically similar concepts in ontology.
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