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One of important functions of Learning Object Metadata (LOM) is to associate XML-based metadata with learning objects. The inherent problem of LOM is that it’s XML specified, which emphasizes syntax and format rather than semantic and knowledge representation. Hence, it lacks the semantic metadata to provide reasoning and inference functions. These functions are necessary for the computer-interpretable descriptions that are critical in the reusability and interoperability of the distributed learning objects. This paper aims at addressing this shortage, and proposes a multi-layered semantic framework to allow the reasoning and inference capabilities to be added to the conventional LOM. To illustrate how this framework work, we developed a Semantic-based Learning Objects Annotations Repository (SLOAR) that offers three different approaches to locate relevant learning objects for an e-learning application LOM-based metadata, ontology-based reasoning, and rule-based inference.

INTEGRATING SEMANTIC WEB REASONING INTO LEARNING OBJECT METADATA