Learning Objects Reusability and Retrieval through Ontological Sharing: A Hybrid Unsupervised Data Mining Approach
Ontologies add semantics and context to learning objects (LOs), enabling LO sharing and reuse in a contextual learning environment and providing better navigation and retrieval of LOs. However, the effectiveness of LO reuse from LO repositories is compromised due to the use of different ontological...
| Main Authors: | , |
|---|---|
| Format: | Book Section |
| Language: | English |
| Published: |
IEEE
2007
|
| Subjects: | |
| Online Access: | http://shdl.mmu.edu.my/3192/ http://shdl.mmu.edu.my/3192/1/Learning%20Objects%20Reusability%20and%20Retrieval%20through%20Ontological%20Sharing%20A%20Hybrid%20Unsupervised%20Data%20Mining%20Approach.pdf |
| _version_ | 1848790259478822912 |
|---|---|
| author | Kiu, Ching-Chieh Lee, Chien-Sing |
| author_facet | Kiu, Ching-Chieh Lee, Chien-Sing |
| author_sort | Kiu, Ching-Chieh |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | Ontologies add semantics and context to learning objects (LOs), enabling LO sharing and reuse in a contextual learning environment and providing better navigation and retrieval of LOs. However, the effectiveness of LO reuse from LO repositories is compromised due to the use of different ontological schemes in each LO repository. This paper presents an algorithmic framework for ontology mapping and merging, OntoDNA, which employs hybrid unsupervised data mining techniques to resolve the semantic and structural differences between ontologies to subsequently create a merged ontology to facilitate LO reuse and retrieval from the Web or from different LO repositories such as ARIADNE, MERLOT, CAREO or Educause. Experimental results on several real ontologies and comparisons with other ontology mapping and merging tools demonstrate the viability of the OntoDNA in terms of precision, recall and f-measure to interoperate LOs in the LO repositories. |
| first_indexed | 2025-11-14T18:09:46Z |
| format | Book Section |
| id | mmu-3192 |
| institution | Multimedia University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:09:46Z |
| publishDate | 2007 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-31922013-11-15T08:29:55Z http://shdl.mmu.edu.my/3192/ Learning Objects Reusability and Retrieval through Ontological Sharing: A Hybrid Unsupervised Data Mining Approach Kiu, Ching-Chieh Lee, Chien-Sing T Technology (General) QA75.5-76.95 Electronic computers. Computer science Ontologies add semantics and context to learning objects (LOs), enabling LO sharing and reuse in a contextual learning environment and providing better navigation and retrieval of LOs. However, the effectiveness of LO reuse from LO repositories is compromised due to the use of different ontological schemes in each LO repository. This paper presents an algorithmic framework for ontology mapping and merging, OntoDNA, which employs hybrid unsupervised data mining techniques to resolve the semantic and structural differences between ontologies to subsequently create a merged ontology to facilitate LO reuse and retrieval from the Web or from different LO repositories such as ARIADNE, MERLOT, CAREO or Educause. Experimental results on several real ontologies and comparisons with other ontology mapping and merging tools demonstrate the viability of the OntoDNA in terms of precision, recall and f-measure to interoperate LOs in the LO repositories. IEEE 2007-07 Book Section NonPeerReviewed text en http://shdl.mmu.edu.my/3192/1/Learning%20Objects%20Reusability%20and%20Retrieval%20through%20Ontological%20Sharing%20A%20Hybrid%20Unsupervised%20Data%20Mining%20Approach.pdf Kiu, Ching-Chieh and Lee, Chien-Sing (2007) Learning Objects Reusability and Retrieval through Ontological Sharing: A Hybrid Unsupervised Data Mining Approach. In: Seventh IEEE International Conference on Advanced Learning Technologies (ICALT 2007). IEEE, pp. 548-550. ISBN 0-7695-2916-X http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4281090 10.1109/ICALT.2007.177 10.1109/ICALT.2007.177 10.1109/ICALT.2007.177 |
| spellingShingle | T Technology (General) QA75.5-76.95 Electronic computers. Computer science Kiu, Ching-Chieh Lee, Chien-Sing Learning Objects Reusability and Retrieval through Ontological Sharing: A Hybrid Unsupervised Data Mining Approach |
| title | Learning Objects Reusability and Retrieval through Ontological Sharing: A Hybrid Unsupervised Data Mining Approach |
| title_full | Learning Objects Reusability and Retrieval through Ontological Sharing: A Hybrid Unsupervised Data Mining Approach |
| title_fullStr | Learning Objects Reusability and Retrieval through Ontological Sharing: A Hybrid Unsupervised Data Mining Approach |
| title_full_unstemmed | Learning Objects Reusability and Retrieval through Ontological Sharing: A Hybrid Unsupervised Data Mining Approach |
| title_short | Learning Objects Reusability and Retrieval through Ontological Sharing: A Hybrid Unsupervised Data Mining Approach |
| title_sort | learning objects reusability and retrieval through ontological sharing: a hybrid unsupervised data mining approach |
| topic | T Technology (General) QA75.5-76.95 Electronic computers. Computer science |
| url | http://shdl.mmu.edu.my/3192/ http://shdl.mmu.edu.my/3192/ http://shdl.mmu.edu.my/3192/ http://shdl.mmu.edu.my/3192/1/Learning%20Objects%20Reusability%20and%20Retrieval%20through%20Ontological%20Sharing%20A%20Hybrid%20Unsupervised%20Data%20Mining%20Approach.pdf |