Abstract
Service discovery plays an important role in service composition. In order to achieve better performance of service discovery, often, service classification should be in place to group available services into different classes. While having a powerful classifier at hand is essential for the task of classification, existing methods usually assume that the class-labels of services are available in prior, which is not true. Traditional clustering methods consume a great deal of time and resources in processing Web service data and result in poor performance, because of the high-dimensional and sparse characteristics of WSDL documents. In this paper, Latent Semantic Analysis (LSA) is combined with the Expectation-Maximization (EM) algorithm to compensate for the poor performance of a single learning model. The obtained class-labels are then used by the Support Vector Machine (SVM) classifier for further classification. We evaluate our approach based on real world WSDL files. The experimental results reveal the effectiveness of the proposed method in terms of accuracy and quality of service clustering and classification.
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Luo, L., Li, L., Wang, Y. (2012). Learning to Classify Service Data with Latent Semantics. In: Li, T., et al. Rough Sets and Knowledge Technology. RSKT 2012. Lecture Notes in Computer Science(), vol 7414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31900-6_35
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DOI: https://doi.org/10.1007/978-3-642-31900-6_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31899-3
Online ISBN: 978-3-642-31900-6
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