Ontological interoperability of learning objects: a hybrid graphical-neural approach

Lee, Chien-Sing and Kiu, Ching-Chieh (2005) Ontological interoperability of learning objects: a hybrid graphical-neural approach. In: World Scientific and Engineering Academy and Society (WSEAS). Stevens Point, Wisconsin, USA. ISBN 960-8457-29-7

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Official URL: http://dl.acm.org/citation.cfm?id=1369661

Abstract

This paper presents OntoShare, an automated ontology mapping and merging architecture for learning object retrieval and reuse. The architecture aims to offer contextual and robust ontology mapping and merging through hybrid unsupervised clustering techniques comprising of Formal Concept Analysis (FCA), Self-Organizing Map (SOM) and K-Means clustering incorporated with linguistic processing using WordNet. The merged ontology facilitates sharing and retrieval of learning objects from the Web or from different learning object repositories such as ARIADNE and Educause. Experimental results can be extended to other resources in databases or data warehouses.

Item Type: Book Section
Additional Information: Proceeding ICCOMP'05 Proceedings of the 9th WSEAS International Conference on Computers Article No. 62
Subjects: T Technology > T Technology (General)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 03 Dec 2013 03:30
Last Modified: 03 Dec 2013 03:30
URI: http://shdl.mmu.edu.my/id/eprint/4529

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