Context-aware applications can satisfy users' information needs without consuming too much time in discovering information adaptive to users. In this paper, we propose a personalized information system to provide more user-oriented information considering context information such as a personal profile with preferences, location, traffic condition, weather, time, event, and so on. Our system architecture is designed to support an effective execution usage on Web services and client applications. We implement a map viewer using a shape type of map format files with Points of Interest(PoI) information. Also, our system can provide associated search results from relations between the objects using context ontologies modeling created by the categorized layers of geospatial data.
"Concerning the second domain referred above, semantic web standards such as RDF/S and OWL (usually referred as ontologies), improved context awareness due to the following reasons   : a) They offer the ability to model complex contextual situations by representing the structure of physical entities and the connections between them. For instance, they are widely used for representing concepts such as persons' profiles, places etc. b) They enable knowledge sharing and interoperability among heterogeneous systems in the web by providing a formal representation standard. "
[Show abstract][Hide abstract] ABSTRACT: In this paper, the design and the implementation of a novel context-aware location based service is presented, called "Geo SPLIS/Geographic Semantic Personalized Location Information System". Geo SPLIS offers users the capability to add their own contextualized preferences regarding Points of Interests (POIs) and combines them with POI owners group targeted offers to deliver high quality personalized information. In order to achieve this, the presented system a) collects data from external sources such as Google Places API, POIs' websites and Google+b) adopts the schema.org ontology to represent people and places profiles, c) provides a user friendly web editor for adding rules at run time, d) uses RuleML and Jess compatible rules to model user preferences and group-targeted place offers and make them machine executable, e) stores data and rules in the Sesame RDF triple store and f) evaluates these data and rules on-the-fly so that to deliver POIs and offers matching user context, presented on Google Maps. Geo SPLIS aims to address some issues regarding knowledge-based personalization in location based services and provide a collaborative knowledge creation platform for other systems in the web.
[Show abstract][Hide abstract] 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.
Proceedings of the 6th international conference on Rules on the Web: research and applications; 08/2012
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