Design and Implementation of a Rule-based Recommender Application Framework for the Semantic Web Data

This paper describes the design and implementation of a recommender application framework which aims to simplify development of ontology-based recommender applications over the Semantic Web data. Recommender system is a type of system that generates meaningful recommendations to support user’s decision. Development of recommender system for the Semantic Web data typically requires ontology, rules and rule-based inference engine to be applied over the RDF data. To facilitate development of recommender applications, our application framework introduces a recommendation template that is a specific form of rule language that provides high-level abstraction in generating recommendations. Recommendation rules can be created based on the template using recommendation editor to hide complexity of rule language syntax. The framework proposes two implementation approaches for generating recommendation results based on the recommendation rules: rule-based reasoner and SPARQL-based implementation. The preliminary evaluation results over a data set in public health domain showed more efficient execution time using the SPARQL-based implementation approach. The results suggested that, by limiting expressiveness of recommendation rules, a specialized form of recommendation processor can be developed for more efficient system performance.