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A linked data compliant framework for dynamic and web-scale consumption of web services

Yu, Hong Qing; Dietze, Stefan; Pedrinaci, Carlos and Liu, Dong (2011). A linked data compliant framework for dynamic and web-scale consumption of web services. International Journal of Computer Information Systems and Industrial Management Applications, 3 pp. 796–803.

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The While Semantic Web Services (SWS) research aims at automating Web service tasks such as discovery, orchestration and execution, its take-up is very limited so far. This is due to several reasons, such as inherent complexity of existing SWS frameworks and the considerable costs involved in creating correct SWS descriptions. In addition, while semantics are in use to enable tasks such as discovery, interaction between service consumers, providers and brokering environments is still not supported by semantic message descriptions. On the other hand, the Linked Data approach has produced a set of established principles for sharing and describing data, such as RDF as representation language and the integral use of dereferencable URIs. In this paper we propose to apply those principles to expose Web services and Web APIs and introduce a framework in which service registries as well as services contribute to the automation of service discovery, and hence, workload is distributed more efficiently. This is achieved by developing a Linked Data compliant Web services framework with that communicate with semi-centralised registries but compute their suitability for a given request themselves. All communications among different framework components are using RDF-based message protocols including service input and output. This framework aims at optimizing load balance and performance by dynamically assembling services at run time in a massively distributed Web environment.

Item Type: Journal Item
Copyright Holders: 2011 MIR Labs
ISSN: 2150-7988
Keywords: linked data, web services, semantic web
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 28899
Depositing User: Kay Dave
Date Deposited: 04 Jul 2011 08:22
Last Modified: 03 May 2019 10:06
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