METEOR-S Web Service Annotation Framework with Machine Learning Classification
Researchers have recognized the need for more expressive descriptions of Web services. Most approaches have suggested using ontologies to either describe the Web services or to annotate syntactical descriptions of Web services. Earlier approaches are typically manual, and the capability to support automatic or semi-automatic annotation is needed. The METEOR-S Web Service Annotation Framework (MWSAF) created at the LSDIS Lab at the University of Georgia leverages schema matching techniques for semi-automatic annotation. In this paper, we present an improved version of MWSAF. Our preliminary investigation indicates that, by replacing the schema matching technique currently used for the categorization with a Naïve Bayesian Classifier, we can match web services with ontologies faster and with higher accuracy.
Digital Object Identifier (DOI)
Lecture Notes in Computer Science, Volume 3387, 2005, pages 137-146.
© Lecture Notes in Computer Science 2005, Springer
Oldham, N., Thomas, C., & Sheth, A. P. (2005). METEOR-S Web Service Annotation Framework with Machine Learning Classification. Lecture Notes in Computer Science, 3387, 137-146.