Data Semantics in Location-Based Services

Abstract. As location-based applications become part of our everyday life, ranging from traffic prediction systems to services over mobile phones providing us with information about our surroundings, the call for more semantics and accurate services is emerging. In this work, we analyze and register the data semantics of Location-based Services (LBS). Initially, we categorize LBS data according to the related concepts and use. We distinguish the (a) Domain Data, including spatial and temporal concepts, namely, position, location, movement and time, (b) Content Data, describing the LBS specific content, and (c) Application Data, consisting of the user profile and the services provided by LBS. Next, we model these three data categories in a way that captures their peculiarities and allows their sharing and exchange among different LBS, when desired. For this, we use semantically rich and expressive models, like UML, as well as the long-praised method of ontologies, realized in the open source, ontology and knowledge-based editor Protégé. To argue about the design choices and show their applicability, we present examples from two characteristic realworld applications, both in the Athens Metropolitan Area: an LBS for tourists carrying mobile devices, and a traffic LBS informing drivers about troublesome situations.