document: Evaluating similarity measures for emergent semantics of social tagging Social bookmarking systems are becoming increasingly important data sources for bootstrapping and maintaining Semantic Web applications . Their emergent information structures have become known as folksonomies . A key question for harvesting semantics from these systems is how to extend and adapt traditional notions of similarity to folksonomies , and which measures are best suited for applications such as community detection , navigation support , semantic search , user profiling and ontology learning . Here we build an evaluation framework to compare various general folksonomy-based similarity measures , which are derived from several established information-theoretic , statistical , and practical measures . Our framework deals generally and symmetrically with users , tags , and resources . For evaluation purposes we focus on similarity between tags and between resources and consider different methods to aggregate annotations across users . After comparing the ability of several tag similarity measures to predict user-created tag relations , we provide an external grounding by user-validated semantic proxies based on WordNet and the Open Directory Project . We also investigate the issue of scalability . We find that mutual information with distributional micro-aggregation across users yields the highest accuracy , but is not scalable ; per-user projection with collaborative aggregation provides the best scalable approach via incremental computations . The results are consistent across resource and tag similarity .
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