Semantic recommendation system of digital educational resources

In today's world, information seekers are confronted with a large volume of very heterogeneous and varied data combined with the multilingual, which makes it difficult to find the most relevant digital educational resource that meets the user's needs. These needs are expressed by a query, generally based on keywords. This observation prompted the researchers to exploit other techniques and methods, among which there is the semantic web. In this paper, we propose a bayesian networks-based recommendation system which represents a recommendation activity. Our goal is to propose an approach to the semantic recommendation of digital resources after each query submitted by the user, by means of SPARQL queries that searches in the Linking Open Data (LOD) cloud.