Abstract
This 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.
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Alves, A.O., Rodrigues, F., Pereira, F.C. (2011). Tagging Space from Information Extraction and Popularity of Points of Interest. In: Keyson, D.V., et al. Ambient Intelligence. AmI 2011. Lecture Notes in Computer Science, vol 7040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25167-2_13
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DOI: https://doi.org/10.1007/978-3-642-25167-2_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25166-5
Online ISBN: 978-3-642-25167-2
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