STC: Semantic Taxonomical Clustering for Service Category Learning
This addresses service discovery in SOA-based systems, but it appears incremental as it builds on existing clustering methods.
The paper tackles the problem of service category learning for service discovery by proposing a self-organizing clustering algorithm called STC, which avoids threshold selection and achieves promising results in classification accuracy and runtime performance on standard datasets.
Service discovery is one of the key problems that has been widely researched in the area of Service Oriented Architecture (SOA) based systems. Service category learning is a technique for efficiently facilitating service discovery. Most approaches for service category learning are based on suitable similarity distance measures using thresholds. Threshold selection is essentially difficult and often leads to unsatisfactory accuracy. In this paper, we have proposed a self-organizing based clustering algorithm called Semantic Taxonomical Clustering (STC) for taxonomically organizing services with self-organizing information and knowledge. We have tested the STC algorithm on both randomly generated data and the standard OWL-S TC dataset. We have observed promising results both in terms of classification accuracy and runtime performance compared to existing approaches.