Urban Knowledge Extraction, Representation and Reasoning as a Bridge from Data City towards Smart City

Abstract

Urban Data management represents a major challenge in the field of Smart Cities. Its understanding is essential for the development of better smart services, which are a persistent demand in urban policies. From all the sources of data available, those that involve a collective processing of urban information (by the citizens or other collectives) deliver in fact, useful insights into social perception. Such is the case, for example, of data collected from mobile networks. Prior to the design of sociotechnical artifacts in cities, it seems important to extract the qualitative and quantitative opinions, sentiment and feedbacks present in these data. In this paper we present three solutions for mining these contents through Knowledge Extraction methods, as a previous step to the prospection of new smart services.