PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Student Modeling Using Principal Component Analysis of SOM Clusters
ICALT '04 Proceedings of the IEEE International Conference on Advanced Learning Technologies
ICALT '05 Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies
Using measures of semantic relatedness for word sense disambiguation
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
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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.