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Contribution to Book
METEOR-S Web Service Annotation Framework with Machine Learning Classification
Lecture Notes in Computer Science
  • Nicole Oldham
  • Christopher Thomas
  • Amit P. Sheth, Wright State University - Main Campus
Document Type
Book Chapter
Publication Date
1-1-2005
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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.

DOI
10.1007/978-3-540-30581-1_12
Citation Information
Nicole Oldham, Christopher Thomas and Amit P. Sheth. "METEOR-S Web Service Annotation Framework with Machine Learning Classification" Lecture Notes in Computer Science Vol. 3387 (2005) p. 137 - 146 ISSN: 9783540243281
Available at: http://works.bepress.com/amit_sheth/241/