Model Driven Architecture: How to Re-model an E-learning Web-based System to Be Ready for the Semantic Web?

HyperManyMedia is a search and browse information retrieval system that provides access to online learning materials. This system offers metadata/ontology-based searching mechanism that allows learners to enter a simple search term and then recommends them with metadata/semantically related terms (concepts/subconcepts). HyperManyMedia integrates several technological components, such as machine learning, clustering (text analysis), knowledge-based modeling (ontology), and graphical data mapping. The first part of this paper proposes an architecture for adding metadata: (1) Domain-knowledge extraction; (2) Parsing learning objects (Lectures) and adding the metadata; (3) Reconfiguring the E-learning platform; and (4) Encapsulating the metadata within the “HyperManyMedia” platform. The second part of this paper proposes an architecture for adding the semantic: (1) Semantic Representation (knowledge representation); (2) Algorithms, which are the core engine of this study; and (3) Personalization to deal with information filtering.