Toward principles for the design of ontologies used for knowledge sharing
International Journal of Human-Computer Studies - Special issue: the role of formal ontology in the information technology
Ontologies for Reusing Learning Object Content
ICALT '05 Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies
Automatic metadata extraction and indexing for reusing e-learning multimedia objects
Workshop on multimedia information retrieval on The many faces of multimedia semantics
Distributed Reasoning for Context-Aware Services through Design of an OWL Meta-Model
ICAS '08 Proceedings of the Fourth International Conference on Autonomic and Autonomous Systems
WBE '08 Proceedings of the Seventh IASTED International Conference on Web-based Education
Learning object repurposing for various multimedia platforms
Multimedia Tools and Applications
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The continuous development of the information and communication technologies makes possible the use of new strategies to support learning processes. In addition, there is an increasingly large number of worldwide available content, which could be used as learning materials. However, in spite of offering an inexhaustible source for search processes, this large number of content makes difficult to find a specific material, appropriated to a particular need. So, the work presented in this paper aims at providing a formal and more comprehensive description of the content embedded in Multimedia Learning Objects (MLOs) through complementary groups of metadata with focus in educational and multimedia properties. Thus, parts of the LOM and MPEG-7 metadata standards, both of them represented in OWL, were used in an architecture of multimedia LO description. This paper argues that through these standards it is possible to describe MLOs in a more appropriate way, increasing the retrieval effectiveness of users' searches and improving the reuse potential of these materials. Moreover, through OWL it is possible to build conceptual links between different multimedia resources as well as perform inference tasks and automatically discovering relationships between them. A prototype and a case study present the functionality of the proposed architecture.