Challenges on Semantic Web Services

Challenges on Semantic Web Services

Maria Vargas-Vera (The Open University, Uk), Miklos Nagy (The Open University, UK), Dominik Zyskowski (Poznan University of Economics, Poland), Konstanty Haniewicz (Poznan University of Economics, Poland) and Witold Abramowicz (Poznan University of Economics, Poland)
DOI: 10.4018/978-1-60566-650-1.ch002


The promise of being able to support Business-to-Customer applications with a rapidly growing number of heterogeneous services available on the Semantic Web has generated considerable interest in different research communities (e.g., Semantic Web, knowledge representation, software agents). However, in order to overcome the challenges of the current Web services, new level of functionalities is required in order to integrate distributed software components using existing Semantic Web standards. In this chapter, the authors discuss and suggest insights into new solutions to the main challenges in the area of Semantic Web services: composition, discovery and trust. For the first problem they suggest to use program transformation coupled with services’ descriptions. For the second problem (discovery of Web services) a solution based on the authors’ mapping algorithm between ontologies is suggested. While, for the last problem a solution based on fuzzy voting model is outlined. Through the chapter, the authors work with an investing scenario, in order to illustrate our suggested solutions to these three challenges.
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Semantic Web Services: An Overview

A Semantic Web service is defined as an extension of Web service description through the Semantic Web annotations, created in order to facilitate the automation of service interactions (McIlraith et al., 2001). Therefore, from the perspective of the functionality offered, Semantic Web services are still Web services. The only difference lays in their description and the consequent benefits that follow, namely the reduction of human involvement in the performed interactions.

Key Terms in this Chapter

Services Composition: In a Service Oriented Architecture (SOA) the operation which aggregates or combines small services into larger services

Dempster-Shafer Theory of Evidence: A statistical uncertain reasoning model which uses belief functions for combining separate pieces of information (evidence) to calculate the probability of an event.

Trust: The ability to assess the credibility of source information based on different criteria

Fuzzy Voting Model: A model which used different voters for fuzzy sets in order to determine the membership value.

Unfold and Fold Operations: Techniques for source level program transformation. Those operations allow to transform clear but inefficient programs into more efficient equivalent programs.

Service discovery: The capability of automatically identifying a software service in Internet which matches the service request criteria.

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