An ontological approach for semantic learning objects interoperability

Che Lee Ming, Hua Tsai Kun, Cheng Hsieh Tung, Kai Chiu Ti, Tzone-I Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - The 7th IEEE International Conference on Advanced Learning Technologies, ICALT 2007
Pages222-226
Number of pages5
DOIs
Publication statusPublished - 2007 Dec 1
Event7th IEEE International Conference on Advanced Learning Technologies, ICALT 2007 - Niigata, Japan
Duration: 2007 Jul 182007 Jul 20

Publication series

NameProceedings - The 7th IEEE International Conference on Advanced Learning Technologies, ICALT 2007

Other

Other7th IEEE International Conference on Advanced Learning Technologies, ICALT 2007
CountryJapan
CityNiigata
Period07-07-1807-07-20

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems and Management

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