METEOR-S Web Service Annotation Framework with Machine Learning Classification

Authors

Nicole Oldham
Christopher Thomas
Amit P. Sheth, Wright State University - Main CampusFollow

Document Type

Book Chapter

Publication Date

2005

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Abstract

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.

Comments

Presented at the First International Workshop on Semantic Web Services and Web Process Composition, San Diego, CA, July 6, 2004.

Repository Citation

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.
https://corescholar.libraries.wright.edu/knoesis/661

DOI

10.1007/978-3-540-30581-1_12