Measurement of Semantic Similarity: A Concept Hierarchy Based Approach
Resolving semantic heterogeneity is one of the major issues in many fields, namely, natural language processing, search engine development, document clustering, geospatial information retrieval and knowledge discovery, etc. Semantic heterogeneity is often considered as an obstacle for realizing full interoperability among diverse datasets. Appropriate measurement metric is essential to properly understand the extent of similarity between concepts. The proposed approach is based on the notion of concept hierarchy which is built using a lexical database. The WordNet, a semantic lexical database, is used here to build the semantic hierarchy. A measurement metric is also proposed to quantify the extent of similarity between a pair of concepts. The work is compared with existing methodologies on Miller Charles benchmark dataset using three correlation coefficients (Pearson's, Spearman's and Kendall Tau rank correlation coefficients) The proposed approach is found to yield better results than most of the existing techniques.