Towards browsing of distant metadata with semantic signature

One of the benefits of an E-Learning network is to connect users to distributed learning repositories where they can be exposed to numerous learning resources. However, metadata of learning resources stored in different repositories are often annotated with concepts defined by different ontologies or classifications specific to their organizations. That makes finding information based on a local conceptual framework difficult. Different organizations with different backgrounds and target audiences may use different terms with similar semantics to define and describe similar learning resources. As such, using a keyword-based approach to find relevant information may not yield satisfactory results. In this thesis, I describe a lightweight information integration solution for browsing federally distributed metadata without incurring expensive schema matching or semantic mapping. I present experiments on real-world data that validate the proposed solution. Finally, I discuss how this approach can simplify semantic mapping and enhance browsing experience in a distributed repository network.