On the combination of textual and semantic descriptions for automated semantic web service classification

Ioannis Katakis, Georgios Meditskos, Grigorios Tsoumakas, Nick Bassiliades, Ioannis Vlahavas

Research output: Chapter in Book/Report/Conference proceedingChapter

20 Citations (Scopus)

Abstract

Semantic Web services have emerged as the solution to the need for automating several aspects related to service-oriented architectures, such as service discovery and composition, and they are realized by combining Semantic Web technologies and Web service standards. In the present paper, we tackle the problem of automated classification of Web services according to their application domain taking into account both the textual description and the semantic annotations of OWL-S advertisements. We present results that we obtained by applying machine learning algorithms on textual and semantic descriptions separately and we propose methods for increasing the overall classification accuracy through an extended feature vector and an ensemble of classifiers.

Original languageEnglish
Title of host publicationArtificial Intelligence Applications and Innovations III
EditorsLazaros Iliadis, Ioannis Vlahavas, Max Bramer
Pages95-104
Number of pages10
DOIs
Publication statusPublished - 31 Jul 2009

Publication series

NameIFIP International Federation for Information Processing
Volume296
ISSN (Print)1571-5736

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  • Cite this

    Katakis, I., Meditskos, G., Tsoumakas, G., Bassiliades, N., & Vlahavas, I. (2009). On the combination of textual and semantic descriptions for automated semantic web service classification. In L. Iliadis, I. Vlahavas, & M. Bramer (Eds.), Artificial Intelligence Applications and Innovations III (pp. 95-104). (IFIP International Federation for Information Processing; Vol. 296). https://doi.org/10.1007/978-1-4419-0221-4_13