A Framework for Web Services Retrieval Using Bio Inspired Clustering

TL;DR

This paper introduces a bio-inspired, self-organizing framework for web services retrieval that dynamically clusters services using semantic similarity, improving efficiency and relevance over traditional methods.

Contribution

It proposes a novel decentralized, bottom-up clustering framework that incorporates semantic relatedness and supports online, dynamic clustering for web services retrieval.

Findings

Enhanced clustering precision due to semantic similarity integration

Reduced computational complexity with decentralized approach

Improved retrieval relevance over traditional clustering methods

Abstract

Efficiently discovering relevant Web services with respect to a specific user query has become a growing challenge owing to the incredible growth in the field of web technologies. In previous works, different clustering models have been used to address these issues. But, most of the traditional clustering techniques are computationally intensive and fail to address all the problems involved. Also, the current standards fail to incorporate the semantic relatedness of Web services during clustering and retrieval resulting in decreased performance. In this paper, we propose a framework for web services retrieval that uses a bottom-up, decentralized and self organising approach to cluster available services. It also provides online, dynamic computation of clusters thus overcoming the drawbacks of traditional clustering methods. We also use the semantic similarity between Web services for the clustering process to enhance the precision and lower the recall.