Coclustering tags and social data sources

Under social tagging systems, a typical Web 2.0 appli-cation, users label digital data sources by using freely cho-sen textual descriptions (tags). Poor retrieval in the afore-mentioned systems remains a major problem mostly due to questionable tag validity and tag ambiguity. Earlier cluster-ing techniques have shown limited improvements, since they were based mostly on tag co-occurrences. In this paper, a co-clustering approach is employed, that exploits joint groups of related tags and social data sources, in which both social and semantic aspects of tags are considered simultaneously. Experimental results demonstrate the effi-ciency and the beneficial outcome of the proposed approach in correlating relevant tags and resources.