The Research Of Web Services Classification Based On Bayesian Technology

Emerging Web standards such as WSDL, SOAP, UDDI and DAML-S promise a network of heterogeneous yet interoperable Web Services. Web Services would greatly simplify the development of many kinds of data integration and knowledge management applications.Web Services are networked components that can be invoked remotely using standard XML-based protocols. The key to automatically invoking and composing Web Services is to associate machine-understandable semantic metadata with each service. A central challenge to the Web Services initiatives is therefore to construct tools to (semi-)automatically generate the necessary metadata. Bayesian technology and Bayesian networks have been successfully used to process artificial intelligence problems and to discover knowledge in databases domain. We explored the specific machine learning technique, i.e. Bayesian technique in this paper, in the hope of automatically creating such kind of metadata from training data.We assigned WSDL documents to different categories using Bayesian Latent Semantic model. With the frame of BLSM, our system classified WSDL documents only by a few of latent class variables and no labeled data. Two steps were included: the first step was to label those documents containing latent class variables by BLSA; the second step was to label the rest by Naive Bayesian model with EM algorithm. It has achieved good precision and proved to be very effective and efficient in our experimental system.