基于自动语义标注和集成学习的Web服务分类

Web Service Classification Based on Automatic Semantic Annotation and Ensemble Learning

随着Web服务技术的发展,它们在互联网上发布的数量正在快速增长,智能地去识别每个Web服务成为了高效运用网络的关键,而识别Web服务的第一步就是对它们进行准确地分类.于是对海量的Web服务进行分类成为一项工作量庞大的任务.于是,为了能够更有效的利用这些Web服务,需要自动对Web服务进行分类.本文以常见的WSDL为例进行研究,由于Web服务的描述采用了WSDL文件,使之无法用传统的文本分类手段.该文中介绍了一种将WSDL文件处理后通过本体匹配进行自动的语义标注,运用Nave Bayes、SVM、REPTree三种分类方法,进而运用集成学习进行分类的方法,在951个Web服务集合上进行19个类别的分类实验中,其准确率达到了87.39%.

With the development of Web Service Technology,the quantity of the web services published on the Internet is increasing rapidly.Recognizing each web service intelligently becomes the key of efficiently using Internet.And the first step of recognization is to classify the web services accurately.To classify a huge amount of web services becomes a difficulty job.Therefore,in order to support applications of web services more effectively,an automatic web service classification method is needed.In this paper,the common WSDL files are regarded as the study object.Since web service is described by WSDL,the traditional document classification method can not be applied directly.In the paper,a new method is proposed which applies automatic web service semantic annotation and uses three classification method： Nave Bayes,SVM and REPTree,furthermore ensemble learning is applied.According to the experiment done on 951 WSDL files and 19 categories,the accuracy was 87.39%