  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.
