Comparative Analysis of Various Semantic Relatedness Measures

Measures of Semantic Relatedness (MSR) quantify the degree in which some words or concepts are related, considering not only similarity but any possible semantic relationship among them. Relatedness computation is of great interest in different areas, such as Natural Language Processing, Information Retrieval, or the Semantic Web. Different methods have been proposed in the past; however, current relatedness measures lack some desirable properties for a new generation of Semantic Web applications:maximum coverage, domain independence and universality. In this paper, semantic relatedness between words is explored using various MSRs and comparative analysis is done between usage of semantic relatedness measures proposed by using WordNet as knowledge corpus and those proposed by using search engines as knowledge corpus. The results clearly indicate the advantage of using search engines based measures in comparison to Wordnet based measures.