Automated Discovery, Categorization and Retrieval of Personalized Semantically Enriched E-learning Resources

In this paper, we describe an integrated and working E-learning search system for retrieving personalized semantically enriched learning resources. Within this context, this work proposes an architecture divided into four layers: (1) Semantic Representation (knowledge representation), (2) Algorithms, which are the core engine of this study, (3) Personalization Interface to deal with information filtering, and (4) Dual representation of the semantic user profile. We use Cluster Analysis in support of an adaptive personalized search for E-learning. This work ends with an experimental evaluation of the results and an overview of future research. Evidence is found that both personalization and semantic enrichment are potential elements for improving an E-learning Information Retrieval System.