Semantic Relatedness Measures in Ontologies Using Information Content and Fuzzy Set Theory

The success of the semantic Web is linked with the use of ontologies on the semantic Web. Ontologies help systems understand the meaning of information and can serve as the interface to the inferencing layer of the semantic Web. An increasingly important task is to determine a degree or measure of semantic relatedness between concepts within and across ontologies. This paper presents an overview of such measures using several examples found in the research literature. The relationship between a distance-based network semantic relatedness measure and an information theoretic measure is shown for the first time by using Tversky´s set-theoretic measures and a new information content measure. New measures of semantic relatedness between ontological concepts are proposed by viewing each concept as a set of its descendent leaf concepts