LORecommendNet: An Ontology-Based Representation of Learning Object Recommendation
One of the most problems facing learners in e-learning system is to find the most suitable course materials or learning objects for their personalized learning space. The main focus of this paper is to extend our previous rule-based representation recommendation system  by applying an ontology-based approach for creating a semantic learning object recommendation named ”LORecommendNet”. The ”LORecommendNet” ontology represents the knowledge about learning objects, learner model, semantic mapping rules and their relationship are proposed. In the proposed framework, we demonstrated how the ”LORecommendNet” can be used to enable machines to interpret and process learning object in recommendation system. We also explain how ontological representations play a role in mapping learner to personalized learning object. The structure of “LORecommendNet” extends the semantic web technology, which the representation of each based on an OWL ontology and then on the inference layer, based on SWRL language, making a clarify separation of the program components and connected explicit modules.
Keywordslearning object ontology recommendation semantic web
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