Abstract
With increasing importance of estimating the semantic similarity between concepts this study tries to highlight some methods used in this area. Similarity measurement between concepts has become a significant component in most intelligent knowledge management applications, especially in fields of Information Extraction (IE) and Information Retrieval (IR). Measuring similarity among concepts has been considered as a quantitative measure of the information; computation of similarity relies on the relations and the properties linked between the concepts in ontology. In this study we have briefly reviewed the main categories of semantic similarity.
Highlights
Semantic similarity measurement techniques have gained great importance with the advent of Semantic Web (Chaves-González and MartíNez-Gil, 2013)
Similarity is calculated based on the target terms to ontology and through testing their relations in ontology (Hliaoutakis et al, 2006)
The depth of a word is not considered if β->α, the semantic density as a monotonically increasing function, which uses the information content of words computed by using a Brown corpus:
Summary
Semantic similarity measurement techniques have gained great importance with the advent of Semantic Web (Chaves-González and MartíNez-Gil, 2013). The depth approach is basically the shortest path approach, but in this technique, the depth of edge that connects two concepts is considered, to quantify the similarity in the general ontology structure; where, it computes the depth, beginning from the root of taxonomy and ending with the intended concepts. Resnik (1995) determines the value of similarity, based on the Information Content of one node (the most informative common subsume), whereas, Jiang and Conrath (1997) use theory of information for determining the weight of every link in a path.
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