DISIT Lab of University of Florence | DISIT Lab of University of Florence

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Title km4city - the Knowledge Model 4 the City (ITA version)

Subject km4city - the Knowledge Model 4 the City (ITA version)

Description The km4city project aims at enabling interconnection, storage and the next interrogation of data from many different sources, such as various portals of the Tuscan region (MIIC, Muoversi in Toscana, Osservatorio dei Trasporti), Open Data provoded by individual municipalities (mainly Florence). It is therefore evident that the ontology will be built, will not be small, and so it may be helpful to view it as consisting of various macro classes, and to be precise, at present, the following macro-categories have been identified: 1. Administration: the first macroclass that is possible to discover, whose main classes are PA, Municipality, Province, Region, Resolution. 2. Street Guide: formed by classes like Road, Node, RoadElement, AdminidtrativeRoad, Milestone, StreetNumber, RoadLink, Junction, Entry, EntryRule and Maneuver. 3. Points of Interest: includes all services, activities, which may be useful to the citizen, and that may have the need to reach. The classification of individual services and activities will be based on classification previously adopted by the Tuscany Region. 4. Local Public Transport: currently we have access to data relating to scheduled times of the leading LPT, the graph rail, and real-time data relating to ATAF services. This macroclass is then formed by many classes like TPLLine, Ride, Route, AVMRecord, RouteSection, BusStopForeast, Lot, BusStop, RouteLink, TPLJunction. 5. Sensors: the macroclass relative to data coming from sensors is developing. Currently in the ontology have been integrated data collected by various sensors installed along some roads of Florence and in that neighbourhood, and those relating to free places in the major parks of the whole region; in our ontology is already present the part relating to events/emergencies, where, however, the collected data are currently very limited in number plus several months old. In addition to these data, in this macroclass were included also data related to Lamma's weather forecast. 6. Temporal: macroclass pointing to include concepts related to time (time instants and time intervals) in the ontology, so that you can associate a timeline to the recorded events and can be able to make predictions.

Technical