Web Services Discovery and Recommendation Based on Information Extraction and Symbolic Reputation

TL;DR

This paper proposes a novel approach for web services discovery and recommendation by combining information extraction from textual descriptions and a new symbolic reputation measure based on web service relationships, improving accuracy and relevance.

Contribution

It introduces a rules-based text tagging method for filtering web service descriptions and a symbolic reputation measure derived from service relationships, enhancing representation and discovery.

Findings

Filtered data improves service representation accuracy.

Symbolic reputation enhances discovery and recommendation.

Experimental results validate the effectiveness of the proposed methods.

Abstract

This paper shows that the problem of web services representation is crucial and analyzes the various factors that influence on it. It presents the traditional representation of web services considering traditional textual descriptions based on the information contained in WSDL files. Unfortunately, textual web services descriptions are dirty and need significant cleaning to keep only useful information. To deal with this problem, we introduce rules based text tagging method, which allows filtering web service description to keep only significant information. A new representation based on such filtered data is then introduced. Many web services have empty descriptions. Also, we consider web services representations based on the WSDL file structure (types, attributes, etc.). Alternatively, we introduce a new representation called symbolic reputation, which is computed from relationships between web services. The impact of the use of these representations on web service discovery and recommendation is studied and discussed in the experimentation using real world web services.