Aggregating metadata to improve access to resources

A major drawback in today´s information landscape is the distribution of resources across numerous repositories. Consequently, users interested in working with these resources need to either jump from repository to repository to find and possibly access them or rely on web search engines. In this paper, we discuss the problems arising from these ways and introduce the metadata based MACE approach. Within the Architecture Higher Education realm, we present the MACE system that uses harvesting to gather all metadata and that employs humans as well as machine learning techniques to semi-automatically relate the resources within the repositories with each other. Thereby, the MACE system provides a portal to find and access relevant architecture learning resources without the need to go from repository to repository. The MACE system provides a number of search facilities like facetted search, geographic-oriented search, classification search and social search to provide its unique access to learning resources. All search functionality bases on metadata that is combined and enriched within the MACE system, portal and community.