Using rules to develop a personalized and social location information system for the Semantic Web

Abstract

In this work, the design and implementation of an innovative context-aware location based social networking service is presented. The proposed system, called "Geosocial SPLIS", utilizes Semantic Web technologies to deliver personalized information to the end user. It addresses some drawbacks of knowledge-based personalization systems and aims to provide a collaborative knowledge creation platform for other systems. To achieve this, it a) collects data from external sources such as Google Places API and Google+ b) adopts the schema.org ontology to represent people and places profiles, c) provides a web editor for adding rules (modeling user preferences and group-targeted place offers) at run time, d) uses RuleML and Jess rules to represent these rules, e) combines at run-time the above to match user context with up to date information, presented on Google Maps and f) matches user's preferences with those of his/her nearby friends to present POI's that are suitable to all of them. All data and rules are stored in the Sesame RDF triple store in order to be shared among various systems. © 2014 Springer International Publishing.