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E-learning has gradually been accepted as an alternative for traditional lecture-based learning. One key factor for the success of e-learning is the possibility of understanding the semantics of learning contents autonomously by machines. The semantic web could naturally fit in according to its ability on information interchange and sharing between machines. Such ability is made possible when the semantic web pages are properly annotated. To transform the existing semantics-lacking learning contents to semantics-enriched ones, we propose a machine learning approach to automatically generate semantic markups for traditional learning contents, which are usually presented in web pages. The proposed method applies the self-organising map algorithm to cluster training web pages and conducts a text mining process to discover the anchor texts to be tagged and their semantic descriptions. Preliminary experiments show that our method may successfully generate semantic markups for the web pages that could be used for e-learning in the semantic web environment.

The semantic Web is gaining popularity for its ability on information interchange and sharing between machines. Such ability is possible when the Web pages are properly annotated. For newly developed semantic Web resources, such annotation can be done manually or by help of sophisticated authoring tools. However, it is not practical to semantically annotating existing Web pages due to the gigantic amount of them. To bridge the gap between the semantic Web and the World Wide Web, we propose a machine learning approach to automatically generate semantic markups for traditional Web pages. The proposed method applies the self-organizing map algorithm to cluster training Web pages and conducts a text mining process to discover the words to be tagged and their semantic descriptions. Preliminary experiments show that our method may successfully generate semantical markups for the Web pages