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Titlebook: Metadata and Semantic Research; Third International Fabio Sartori,Miguel Ángel Sicilia,Nikos Manouseli Conference proceedings 2009 Springe

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Empirical Analysis of Errors on Human-Generated Learning Objects Metadataills, unvoluntary mistakes, lack of information, for instance). This paper analyses human-generated learning object metadata records described according to the IEEE LOM standard, identifies the most significant errors committed and points out which parts of the standard should be improved for the sake of quality.
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Clarifying the Semantics of Relationships between Learning Objectsructure of LOs. As main contribution, we develop a taxonomy of possible relationships between LOs that has been created by refining the LOM standard relationships with other meaningful relationships from a common sense ontology.
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A Framework for Automatizing and Optimizing the Selection of Indexing Algorithmsithms which detect the requested features. The application of our proposed framework is illustrated through the case of an RDF-based information system. In this case, our approach could be further optimized by a broader integration of Semantic Web technologies.
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Ontological Enrichment of the , Databases resources. In G2SBC database ontology terms both provide a semantic layer to improve data storage, accessibility and analysis and represent a user friendly instrument to identify relations among biological components.
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