Abstract:
Web services have been evolved as a versatile and cost effective solution for
exchanging dissimilar data between distributed applications. They have become a
fundamental part of service oriented architecture (SOA). The most challenging
problem being faced by service oriented architecture is to figure out what a service
does and how to use its capabilities without direct negotiation with service provider.
Discovering and exploring web services registered with UDDI registry or Web
Services-Inspection (WS-Inspection) documents requires exact search criteria such as
service category, service name and service URL.
This study focuses on creating a smarter automated web service classification
technique by applying Maximum Entropy machine learning algorithm to attributes of
Web Service Description language (WSDL) documents. WSDL document allows web
services clients to learn operations, communication protocols and correct message
format of service. Manually analyzing WSDL documents is the best approach but
most expensive. Therefore a text mining based approach is suggested for classifying
web services into functional groups.
