Ontology-based content model for scalable content reuse

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Ontology-based content model for scalable content reuse

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International Conference On Knowledge Capture archive
Proceedings of the 4th international conference on Knowledge capture table of contents

Whistler, BC, Canada

POSTER SESSION: Posters table of contents

Pages: 195 - 196

Year of Publication: 2007

ISBN:978-1-59593-643-1

Authors

Sasa Nesic	University of Lugano: Faculty of Informatics, Lugano, Switzerland
Jelena Jovanovic	University of Belgrade, Belgrade, Serbia
Dragan Gasevic	Athabasca University, Athabasca, Canada
Mehdi Jazayeri	University of Lugano: Faculty of Informatics, Lugano, Switzerland

Sponsors

SIGART: ACM Special Interest Group on Artificial Intelligence
ACM: Association for Computing Machinery

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ACM New York, NY, USA

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Downloads (6 Weeks): 8, Downloads (12 Months): 64, Citation Count: 0

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ABSTRACT

The paper presents Abstract Compound Content Model (ACCM), a generic content model which we have devel-oped aiming to facilitate interoperability, repurposing and integration of diverse platform specific content models. Based on this model we have developed the ACCM ontology in order to turn the ACCM's elements (i.e., content units and content aggregations) into resources that can be directly accessed and thus reused. The paper also presents our current work on the implementation of an ACCM-based content management system that enables efficient storage, indexing, search and retrieval of content units as they are defined in the ACCM ontology.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Dragan Gaaevic , Dragan Djuric , Vladan Devedzic , Bran Selic, Model Driven Architecture and Ontology Development, Springer-Verlag New York, Inc., Secaucus, NJ, 2006
	2	Verbert, K., Jovanovi , J., Duval, E., Gaaevi , D., Meire, M., "Ontology-based Learning Content Repurposing: the ALOCoM Framework," International Journal on E-Learning, Vol. 5, No. 1, 2006, pp. 67--74.
	3	Siegfried Handschuh , Raphael Volz , Steffen Staab, Annotation for the Deep Web, IEEE Intelligent Systems, v.18 n.5, p.42-48, September 2003 [doi>10.1109/MIS.2003.1234768]
	4	Jovanovi , J., Gaaevi , D., Deved i , V., "Ontology-based Automatic Annotation of Learning Content," Int5rnational Journal on Semantic Web and Information Systems, Vol. 2, No. 2, 2006, pp. 91--119.