A metametadata taxonomy to support semantic searching algorithms in metadata repository

This paper discusses on a novel method for semantic searching and retrieval of information about learning materials. A novel metametadata taxonomy has been developed which provides the basis for a semantic search engine to extract, match and map queries to retrieve relevant results. Metametadata encapsulate metadata instances by using the properties and attributes provided by ontologies rather than describing learning objects. The use of ontological views assists the pedagogical content of metadata extracted from learning objects by using the control vocabularies as identified from the metametadata taxonomy. The use of ontological approach and metametadata (based on the metametadata taxonomy) have contributed towards a novel semantic searching mechanism. These three strands - the taxonomy, the ontological views, and the search algorithm - are integrated into an architecture (OMESCOD) to support the semantic search within the metadata repository.