Web Service Classification Using Support Vector Machine

Classification is a widely used mechanism for facilitating Web service discovery. Existing methods for automatic Web service classification only consider the case where the category set is small. When the category set is big, the conventional classification methods usually require a large sample collection, which is hardly available in real world settings. This paper presents a novel method to conduct service classification with a medium or big category set. It uses the descriptive information of categories in a large-scale taxonomy as sample data, so as to disengage from the dependence on sample service documents. A new feature selection method is introduced to enable efficient classification using this new type of sample data. We demonstrate the effectiveness of our classification method through extensive experiments.