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Discovering Substitutable and Composable Semantic Web Services for Web Service Composition

by A.bhuvaneswari, G.r.karpagam
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 48 - Number 8
Year of Publication: 2012
Authors: A.bhuvaneswari, G.r.karpagam
10.5120/7365-9988

A.bhuvaneswari, G.r.karpagam . Discovering Substitutable and Composable Semantic Web Services for Web Service Composition. International Journal of Computer Applications. 48, 8 ( June 2012), 1-8. DOI=10.5120/7365-9988

@article{ 10.5120/7365-9988,
author = { A.bhuvaneswari, G.r.karpagam },
title = { Discovering Substitutable and Composable Semantic Web Services for Web Service Composition },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 48 },
number = { 8 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume48/number8/7365-9988/ },
doi = { 10.5120/7365-9988 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:43:31.460512+05:30
%A A.bhuvaneswari
%A G.r.karpagam
%T Discovering Substitutable and Composable Semantic Web Services for Web Service Composition
%J International Journal of Computer Applications
%@ 0975-8887
%V 48
%N 8
%P 1-8
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Semantic Web Service Discovery is an indispensable part of Web Service Composition which is expected to (i) match the user request against multiple service advertisements and (ii) provide a set of Substitutable and Composable services by maintaining the relationship among services. This paper employs Indirect Backward Chaining Matchmaking approach to match against multiple advertisements and utilizes hash tables to maintain the relationship among services. The experimentation is done with the online product purchase scenario and the results are shown by considering (i) Input and Output only, (ii) Input, Output, Precondition and Effect and (iii) Data type of the input and output concepts. The quantitative evaluation is carried out based on the information retrieval measures recall, precision and F-measure.

References
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  11. precision is the fraction of retrieved instances that are relevant, while recall is the fraction of relevant instances that are retrieved available at http://en. wikipedia. org/wiki/Precision_and_recall.
Index Terms

Computer Science
Information Sciences

Keywords

Semantic Matchmaking Indirect Backward Chaining Substitutability Composability Recall Precision

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