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

Authors: Valerie Cross; Youbo Wang;

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

Abstract

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

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    16
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    influence
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
16
Average
Top 10%
Top 10%
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