Web Service Classification

Introduction

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. 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.

Datasets

We used the OWLS-TC ver. 2.2 collection that consists of 1007 OWL-S advertisements. The collection is accompanied with a set of 23 ontologies that are used to annotate the I/O parameters. The advertisements are also preclassified in seven categories, namely Travel, Education, Weapon, Food, Economy, Communication, and Medical. Please note that this collection is an artificial one. However, it is the only publicly available collection with a relatively large number of advertisements, and it has been used in many research efforts. After a preprocessing of the collection we obtained 395 distinct concepts and 456 distinct words. We have extracted features with various methods and we make available six different versions of the dataset in WEKA (.arff) format that can be used with any machine learning algorithm for automated web service classification. We will later provide detailed descriptions of the below datasets. For more information please contact Ioannis Katakis and/or Georgios Meditskos.

Publications

I. Katakis, G. Meditskos, G. Tsoumakas, N. Bassiliades, I. Vlahavas, “ On the Combination of Textual and Semantic Descriptions for Automated Semantic Web Service Classification”, Proceedings of the 5th IFIP Conference on Artificial Intelligence Applications & Innovations (AIAI 2009), Springer, Thessaloniki, 2009.