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A network based semantic similarity measure for knowledge management in social tagging systems

As the social tagging systems becoming prevalent, it remains a critical question that how to make explicit the semantics for tags to fully facilitate Web2.0 applications. This paper introduces a pair of customized operators for the network of semantic similarity among tags, i.e. NSST, constructs a network based measure to evaluate the semantic similarity among random pair of tags, and presents a case study using the empirical data retrieved from delicious website. Comparing to the traditional measures, our network based measure is capable of evaluating similarities between random tags even not co-occurred, can better reflect the structural influence of the network of tag co-occurrence, and is feasible for applications like intelligent search in user-centric Web2.0 environment.