Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
An Approach to support Web Service Classification and Annotation
EEE '05 Proceedings of the 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'05) on e-Technology, e-Commerce and e-Service
Tensor space model for document analysis
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Rough set Based Ensemble Classifier forWeb Page Classification
Fundamenta Informaticae
A kernel based structure matching for web services search
Proceedings of the 16th international conference on World Wide Web
Similarity search for web services
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
METEOR-S web service annotation framework with machine learning classification
SWSWPC'04 Proceedings of the First international conference on Semantic Web Services and Web Process Composition
Tensor Framework and Combined Symmetry for Hypertext Mining
Fundamenta Informaticae
Tensor Framework and Combined Symmetry for Hypertext Mining
Fundamenta Informaticae
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The transition of the World Wide Web from a paradigm of static Web pages to one of dynamic Web services raises a new and challenging problem of locating desired web services. With the expected growth of the number of Web services available on the web, the need for mechanisms that enable the automatic categorization to organize this vast amount of data, becomes important. In this paper we propose Tensor space model for data representation and Rough Set based approach for the classification of Web services. The proposed tensor space model captures the information from internal structure of WSDL documents along with the corresponding text content. Rough sets are used here to combine information of the individual tensor components for providing classification results. Two step improvement on the existing classification results of web services has been shown here. In the first step we achieve better classification results over existing, by using proposed tensor space model. In the second step further improvement of the results has been obtained by using Rough set based ensemble classifier.