Investigating Ontological Similarity Theoretically with Fuzzy Set Theory, Information Content, and Tversky Similarity and Empirically with the Gene Ontology

This paper theoretically and empirically investigates ontological similarity. Tversky's parameterized ratio model of similarity [3] is shown as a unifying basis of many of the well-known ontological similarity measures. A new family of ontological similarity measures is proposed that allows parameterizing the characteristic set used to represent an ontological concept. The three subontologies of the prominent GO are used in an empirical investigation of several ontological similarity measures. A new ontological similarity measure derived from the proposed family is also empirically studied. A detailed discussion of the correlation among the measures is presented as well as a comparison of the effects of two different methods of determining a concept's information content, corpus-based and ontology-based.

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