Abstract
AbstractThis paper is about automatic tagging of urban areas considering its constituent Points of Interest. First, our approach geographically clusters places that offer similar services in the same generic category (e.g. Food & Dining; Entertainment & Arts) in order to identify specialized zones in the urban context. Then, these places are analysed and tagged from available information sources on the Web using KUSCO [2,3] and finally the most relevant tags are chosen considering not only the place itself but also its popularity in social networks. We present some experiments in the greater metropolitan area of Boston.KeywordsContext-AwarenessSemantic EnrichmentWeb Mining
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