Bridging the WWW to the Semantic Web by Automatic Semantic Tagging of Web Pages

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