On Flexible Web Services Composition Networks

TL;DR

This paper investigates the use of three similarity metrics—Levenshtein, Jaro, and Jaro-Winkler—in constructing syntactic Web services networks, comparing their effectiveness through topological analysis of real-world data.

Contribution

It provides a comparative analysis of similarity metrics for syntactic Web service networks, highlighting the suitability of Jaro-Winkler and Jaro at different thresholds.

Findings

Jaro-Winkler performs best at higher thresholds.

Jaro detects fewer irrelevant relationships at lower thresholds.

Networks' topological properties vary with the similarity metric used.

Abstract

The semantic Web service community develops efforts to bring semantics to Web service descriptions and allow automatic discovery and composition. However, there is no widespread adoption of such descriptions yet, because semantically defining Web services is highly complicated and costly. As a result, production Web services still rely on syntactic descriptions, key-word based discovery and predefined compositions. Hence, more advanced research on syntactic Web services is still ongoing. In this work we build syntactic composition Web services networks with three well known similarity metrics, namely Levenshtein, Jaro and Jaro-Winkler. We perform a comparative study on the metrics performance by studying the topological properties of networks built from a test collection of real-world descriptions. It appears Jaro-Winkler finds more appropriate similarities and can be used at higher thresholds. For lower thresholds, the Jaro metric would be preferable because it detect less irrelevant relationships.