Towards Effectively Identifying RESTful Web Services

Abstract

In recent years, RESTful Web services have been rapidly developed and deployed, because of the advantages of lightweight, flexibility and extensibility, etc. However, most RESTful services are described in heterogeneous and ordinary HTML pages, which makes them really difficult to be identified and crawled automatically from the Internet. In this paper we propose a hybrid classifier framework called co-NV for automatic identification of RESTful services on the Web. In our framework, web pages are analyzed and filtered according to the contents and structure characteristics of HTML documents, with Naïve Bayes classifier and Vector Space Model (VSM) respectively. Experiments with real RESTful services prove that our framework works effectively with high precision and recall rate, and is very practical.