STC: Semantic Taxonomical Clustering for Service Category Learning

TL;DR

This paper introduces STC, a self-organizing clustering algorithm that improves service category learning by taxonomically organizing services, addressing threshold selection issues and enhancing accuracy and efficiency.

Contribution

The paper proposes a novel self-organizing clustering method, Semantic Taxonomical Clustering (STC), for service category learning in SOA systems, improving over existing similarity-based approaches.

Findings

STC achieves higher classification accuracy.

STC demonstrates faster runtime performance.

Promising results on both synthetic and real datasets.

Abstract

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.