A Hybrid Supervised/Unsupervised Machine Learning Approach to Classify Web Services
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Abstract:

Reusing software is a promising way to reduce software development costs. Nowadays, applications compose available web services to build new software products. In this context, service composition faces the challenge of proper service selection. This paper presents a model for classifying web services. The service dataset has been collected from the well-known public service registry called ProgrammableWeb.

The results were obtained by breaking service classification into a two-step process. First, Natural Language Processing(NLP) pre-processed web service data have clustered by the Agglomerative hierarchical clustering algorithm. Second, several supervised learning algorithms have been applied to determine service categories.

The findings show that the hybrid approach using the combination of hierarchical clustering and SVM provides acceptable results in comparison with other unsupervised/supervised combinations.

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