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Call for Paper - May 2015 Edition
IJCA solicits original research papers for the May 2015 Edition. Last date of manuscript submission is April 20, 2015. Read More

Context Aware Web Service Discovery for Service Composition using Term Expansion and Context Analysis

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International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 114 - Number 15
Year of Publication: 2015
Authors:
Priya S
Saravana Balaji B
10.5120/20056-2022

Priya S and Saravana Balaji B. Article: Context Aware Web Service Discovery for Service Composition using Term Expansion and Context Analysis. International Journal of Computer Applications 114(15):30-34, March 2015. Full text available. BibTeX

@article{key:article,
	author = {Priya S and Saravana Balaji B},
	title = {Article: Context Aware Web Service Discovery for Service Composition using Term Expansion and Context Analysis},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {114},
	number = {15},
	pages = {30-34},
	month = {March},
	note = {Full text available}
}

Abstract

Webservices are essential in business-to-business enterprise integration and distributed computing. Webservices descriptions are expressed in XML standards which represent only syntactic representation of the input and output of the service. It does not provide relationships between the concepts and the actual meaning of the terms. As the number of services are increased along with user requirement, determining the appropriate service becomes a challenging task. Semantic web services are proposed to overcome this challenge thereby recommending the suitable services to the user. In this paper, we propose Context-aware Webservice discovery to find the qualified services for service composition. This approach improve the matchmaking performance by expanding the terms and performing context matching of the input and output parameters thereby the matched services are compatible with user's contextual expectation. The experimental results implies that the proposed approach is better than other alternatives under precision and recall matrix.

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