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 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.
Keywords
Semantics of Place, Information Extraction, Clustering, Spatial Analysis
Subject
Ambient Intelligence
Conference
International Joint Conference on Ambient Intelligence, November 2011
DOI
Cited by
Year 2018 : 1 citations
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Year 2017 : 1 citations
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Year 2016 : 1 citations
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Year 2015 : 2 citations
Xiao Han. "Mining user similarity in online social networks : analysis,modeling and applications". PhD Thesis. Institut National des Télécommunications, 2015. English
Jitao Sang, Tao Mei, and Changsheng Xu. 2015. Activity Sensor: Check-In Usage Mining for Local Recommendation. ACM Trans. Intell. Syst. Technol. 6, 3, Article 41 (April 2015), 24 pages. DOI=10.1145/2700468 http://doi.acm.org/10.1145/2700468
Year 2013 : 2 citations
Sang, Jitao, Tao Mei, Changsheng Xu and Shipeng Li. "Contextual and Personalized Mobile Recommendation Systems." Tools for Mobile Multimedia Programming and Development. IGI Global, 2013. 82-97. Web. 7 Jan. 2015. doi:10.4018/978-1-4666-4054-2.ch005
Chilooo Gachilio(2013). A Study on the Construction Method of POI Data Using SLI and Vector Map Fusion. Engineering Doctoral Thesis. Seoul National University Graduate School
