Abstract
Semantic similarity is used to perceive the meaning of words in textual data and having several applications in the field of Computational linguistic and Natural language processing. Semantic similarity and semantic relatedness are interchangeable terms, in Semantic relatedness all types of semantic relationships are considered i.e. (has, part-of, contains etc.) whereas in semantic similarity only “is-a” type of relationship is used. Computing accurate similarity improves the efficiency of query processing and understanding of textual data more proficiently. In this work, a novel hybrid approach of corpus based and knowledge based method is proposed approach to compute the semantic relatedness among words. In this hybrid method, Latent semantic analysis is used to find the concepts and their correlation value and mapping is done for different words with the help of fuzzy formal concept analysis.To compute semantic relatedness on different words a fuzzy set similarity measure is used.The proposed approach has been evaluated on solar domain and attains improved results as compared to other baseline measures. This method can be used for any doamin as it is a generalized approach for similarity computation.
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