Abstract
This article introduces a n-gram-based approach to automatic classification of Web services using a multilayer perceptron-type artificial neural network. Web services contain information that is useful for achieving a classification based on its functionality. The approach relies on word n-grams extracted from the web service description to determine its membership in a category. The experimentation carried out shows promising results, achieving a classification with a measure F=0.995 using unigrams (2-grams) of words (characteristics composed of a lexical unit) and a TF-IDF weight.
Highlights
Web services are reusable software components through which you can build and integrate new applications without having to deploy all the elements of a system
There are several public Web service repositories: a) the SOAP Web Services directory supported by Membrane; b) the Visual Web Service Web Services repository; c) the XMethods Web service repository; d) Programmable Web; e) OWLS-TC is a collection of test services recovered with their respective annotations on OWL-S [1]
Web service descriptions are made using the standard Web Service Description Language (WSDL) language, such description consists of an XML-based text file, within which the elements required for service invocation [2] are defined
Summary
Web services are reusable software components through which you can build and integrate new applications without having to deploy all the elements of a system. This article introduces a n-gram-based approach to automatic classification of Web services using a multilayer perceptron-type artificial neural network.
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