Ontology and Rule-Based Recommender System for E-learning Applications

Bouchra Bouihi, Mohamed Bahaj

Abstract


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).

Keywords


Semantic web; Recommendation System;Ontology;E-learning;SWRL

Full Text:

PDF


Copyright (c) 2019 Bouchra BOUIHI, Mohamed Bahaj


International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
Creative Commons License
Indexing:
Scopus logo Clarivate Analyatics ESCI logo EI Compendex logo IET Inspec logo DOAJ logo DBLP logo Learntechlib logo EBSCO logo Ulrich's logo Google Scholar logo MAS logo