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Personalized support for learners becomes even more important, when e-Learning takes place in open and dynamic learning and information networks. This paper shows how to realize personalized learning support in distributed learning environments based on Semantic Web technologies. Our approach fills the existing gap between current adaptive educational systems with well-established personalization functionality, and open, dynamic learning repository networks. We propose a service-based architecture for establishing personalized e-Learning, where personalization functionality is provided by various web-services. A Personal Learning Assistant integrates personalization services and other supporting services, and provides the personalized access to learning resources in an e-Learning network.

Abstract. Despite the fact that adaptive hypermedia techniques have proven their ability to provide user guidance and orientation in hyperspace, we do not currently see the widespread adoption of these techniques. A couple of reasons may explain this phenomenon. One of them is the current lack of re-usability and interoperability between adaptive techniques/systems, which -to some degree -originates in the so-called "open corpus problem" found in adaptive hypermedia. In this article, we analyze this problem in a popular arena: adaptive hypermedia systems with an emphasis on education. The origins and effects of the open corpus problem are discussed, and recent approaches are demonstrated that have -in one way or the other -developed as strategies for solving the open corpus problem. We summarize these findings and discuss how solution strategies can be successfully employed in the future, enabling adaptive hypermedia techniques within open, dynamic information spaces, such as the Semantic Web.

Abstract. Semantic Web databases allow efficient storage and access to RDF statements. Applications are able to use expressive query languages in order to retrieve relevant metadata to perform different tasks. However, access to metadata may not be public to just any application or service. Instead, powerful and flexible mechanisms for protecting sets of RDF statements are required for many Semantic Web applications. Unfortunately, current RDF stores do not provide fine-grained protection. This paper fills this gap and presents a mechanism by which complex and expressive policies can be specified in order to protect access to metadata in multi-service environments.