An Ontological Approach for Semantic Learning Objects Interoperability

This paper presents a semantic-aware classification algorithm that can leverage the interoperability among semantically heterogeneous learning object repositories using different ontologies. The proposed algorithm is to map sharable learning objects, using meanings instead of just keyword matching, from heterogeneous repositories into a local knowledge base (an e-learning ontology). Significance of this research lies in the semantic inferring rules for learning objects classification as well as the full automatic processing and self-optimizing capability. This approach is sufficiently generic to be embedded into other e-learning platforms for semantic interoperability among learning object repositories. Focused on digital learning material and contrasted to other traditional classification technologies, the proposed approach has experimentally demonstrated significantly improvement in performance.

• Type: Conference
• Year: 2007
• Where: ICALT
• Authors: Ming-Che Lee, Kun Hua Tsai, Tung Cheng Hsieh, Ti Kai Chiu, Tzone I. Wang
• Keywords: Heterogeneous Learning Object, ICALT 2007, Learning Object, Machine Learning, Object Repositories