Improving REST Service Discovery with Unsupervised Learning Techniques

Abstract

Discovery and replacement are two of the main features of Service Oriented Computing. There has been much research on these topics for traditional SOAP-based Web Services, particularly on discovery. Although the original proposal for REST services lacks this feature, some researchers have studied how to perform discovery for REST services using both IR based techniques and semantic techniques. This work presents a novel IR-based discovery approach for REST services described via WADL files. Our approach takes advantage of unsupervised machine learning techniques for improving discovering results. In particular, the approach relies on clustering algorithms, such as K-means or X-means, to reduce the search space for a given query. The experimental results show that using an appropriate clustering technique, our approach achieves nearly 4 times higher F-measure than a traditional IR-based search engine, namely Apache Lucene. Additionally, the paper reports other metrics, such as Recall, Precision, Precision at-10 and Recall at-10, that also point out that the proposed approach outperforms Lucene. Finally, another important contribution is a set of queries and WADL files gathered from the Internet that can be used for evaluating future discovery proposals.