Classifying Web Services With and Without Association Rules

Abstract

The transition of the World Wide Web from a paradigm of static Web pages to one of dynamic Web Services raises a new and challenging problem of locating desired Web Services. With the expected growth of the number of Web Services available on the web, the need for mechanisms that enable the automatic categorization to organize this vast amount of data becomes important. Web Services classification is the task of automatically sorting a set of documents into categories from a predefined set. Automated Web Services classification is attractive because it removes the need of manually organizing document bases, which can be too expensive, or simply not feasible given the time constraints of the application or the number of documents involved. The process involves text mining and classification of WSDL (Web Service Description Language) documents based on Association rules. Text mining techniques are used at the first stage, namely preprocessing, to extract relevant information from a WSDL documents. Textual documentation, operations and arguments accompanying descriptions of Web Services are preprocessed. Association rules are applied to analyze the degree of dependency between contents of WSDL and category of the Web Services. A machine learning classifier is used at the last stage to categorize the documents under different categories. This classifier deduces a sequence of candidate categories for a preprocessed Web service description. Here the concept of Association rules in context of Web Services is used and its performance is compared with the primitive algorithms for classification.