Ontology and Rule-Based Recommender System for E-learning Applications
DOI:
https://doi.org/10.3991/ijet.v14i15.10566Keywords:
Semantic web, Recommendation System, Ontology, E-learning, SWRLAbstract
The continuous growth of the internet has given rise to an overwhelming mass of learning materials. Which has increased the demand for a recommendation system to filter information and to deliver the learning materials that fit learners learning context. In this paper, we propose an architecture of a semantic web based recommender system. The proposed architecture is a redesigned architecture of the classical 3-tiers web application architecture with an additional semantic layer. This layer holds two semantic subsystems: an Ontology-based subsystem and SWRL (Semantic Web Rule Language) rules one. The Ontology subsystem is used as a reusable and sharable domain knowledge to model the learning content and context. The SWRL rules are used as a recommendation and filtering technique based on learning object relevance and weighting. These rules are organized into four categories: Learning History Rules (LHR), Learning Performance Rules (LPR), Learning Social Network Rules (LSNR) and Learning Pathway Rules (PR).
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Published
2019-08-01
How to Cite
Bouihi, B., & Bahaj, M. (2019). Ontology and Rule-Based Recommender System for E-learning Applications. International Journal of Emerging Technologies in Learning (iJET), 14(15), pp. 4–13. https://doi.org/10.3991/ijet.v14i15.10566
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Papers