A. Bikakis and A. Giurca (Eds.): RuleML 2012, LNCS 7438, pp. 215–223, 2012. © Springer-Verlag Berlin Heidelberg 2012 Personalizing Location Information through Rule-Based Policies Iosif Viktoratos 1 , Athanasios Tsadiras 1 , and Nick Bassiliades 2 1 Department of Economics, Aristotle University of Thessaloniki GR-54124 Thessaloniki, Greece 2 Department of Informatics, Aristotle University of Thessaloniki GR-54124 Thessaloniki, Greece {viktorat,tsadiras,nbassili}@auth.gr Abstract. In this paper, the idea of providing personalized, location-based information services via rule-based policies is demonstrated. After a short introduction about related technologies and approaches, an innovative Personalized Location Information System (PLIS) is designed and implemented. PLIS delivers personalized and contextualized information to users according to rule-based policies. More specifically, many categories of points of interest (e.g. shops, restaurants) have rule-based policies to expose and deploy their marketing strategy on special offers, discounts, etc. PLIS evaluates these rules on-the-fly and delivers personalized information according to the user’s context and the corresponding rules fired within this context. After discussing the design and the implementation of PLIS, illustrative examples of PLIS functionality are presented. As a result, PLIS proves that combining contextual data and rules can lead to powerful personalized information services. Keywords: RuleML, Rules, Location Based Services, Context, Points of Interest, Jess. 1 Introduction 1.1 Rules and Policies Rule-based policies are an important sector of our everyday life. They are used consistently by various types of businesses (or in general, Points of Interest-POI in our Location- based context), not only to deploy their marketing strategy, but also to expose them to the public in a comprehensible manner. A constraint is that, such kind of policies, have to be translated into a computer understandable language, in order to be executed and adopted by an information service [1]. As a result, a general rule language is needed for this purpose. After various initials efforts in conventional languages [1], RIF was adopted as a general rule language [2] by the World Wide Web Consortium (W3C). RIF was influenced by a previous but still ongoing rule standardization initiative called RuleML [3]. RuleML is a family of sublanguages which are used to publish rules on