Semantic Based Intelligent Resource Service Composition Technology

Yi Jing Zhao; Yan Yan; Guo Xin Wang; Jia Hao; Si Chen

doi:10.4028/www.scientific.net/AMR.591-593.2456

Paper Titles

Discussion about the Efficient Management and Control of Construction Projects′ Cost
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Diagnostic Strategy Optimization Based on Predatory Search Algorithm
p.2441

Safe Comprehensive Evaluation of Fully Mechanized Mining Face Based on Fuzzy Matter-Element
p.2445

Relationship Study of Absorption Capacity, Degree of Innovation and Innovative Performance in Textile and Garment Enterprises
p.2450

Semantic Based Intelligent Resource Service Composition Technology
p.2456

Research on the Dangerous Quality of Roasting Lighter for Electric Heater
p.2461

Constitutive Model Based on the Multiplicative Decomposition of Deformation Gradient in Arbitrary Orthogonal Coordinate System
p.2465

Research on Planning of Logistics System in the Material Support Base for Emergency
p.2474

Artificial Bee Colony Algorithm for the Parallel Test Tasks Scheduling
p.2478

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 591-593Semantic Based Intelligent Resource Service...

Semantic Based Intelligent Resource Service Composition Technology

Abstract:

Intelligent resource includes the knowledge, hardware and software resources even workflow and human resource, etc. From the point of view of enterprise information management, the service composition is an essential element for companies’ informatization construction. Therefore, the paper describes a web service composition framework for semantically-annotated web service. Based on the state of art ontology technology, an OWL-S process model for the CAM software is established to describe resource service. Then a service discovery process which mainly consists of services’ property matching algorithm for words is set up. A backward chaining framework for service composition is employed to dynamically compose resource services finally.

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Periodical:

Advanced Materials Research (Volumes 591-593)

Pages:

2456-2460

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.591-593.2456

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Online since:

November 2012

Authors:

Yi Jing Zhao, Yan Yan, Guo Xin Wang, Jia Hao, Si Chen

Keywords:

Backward Chaining, Ontology, Semantic, Service Composition

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References

[1] F. Tao, D.M. Zhao, Y.F. Hu and Z.D. Zhou: IEEE transactions on Industrial Information, vol. 4 (2008) No.4, pp.315-327.

Google Scholar

[2] N. Milanovic and M. Malek: Internet Computing, vol. 8 (2004) No.6, pp.51-59.

Google Scholar

[3] C.L. Sun, Y. Lin and B. Kemme: Proc. IEEE International Conference on Service Computing (Washington, USA, July 6-9, 2004), pp.218-225.

Google Scholar

[4] C.Y. Dai, M.K. Dai and H.J. Zhang: Computer Engineering and Design, vol. 30 (2009) No.2, pp.358-360.

Google Scholar

[5] F. Tao: Study on Manufacturing Grid Resource Service Optimal-Allocation Theory and Application (Ph.D., Wuhan Univ. of Technology, China, 2008), p.60.

Google Scholar

[6] S.L. Pan, Y.X. Zhang and X.F. Yu: Computer Engineering, vol. 36 (2010) No.12, pp.48-51.

Google Scholar

[7] Information on: http://www.w3.org/2004/OWL.2008-06-02.

Google Scholar

[8] Y.P. Yang: Research on Key Technologies of Automatic Web Services Composition (Ph.D., National Univ. of Defense Technology, China, 2007), p.32.

Google Scholar

[9] The OWL Services Coalition. OWL-S: Semantic Markup for Web Services. In Technical White paper (OWL-S version 1.0), 2003.

Google Scholar

[10] J.M. Liu, N. Gu and B.L. Shi: Journal of Computer Research and Development, vol. 42 (2005) No.7, pp.1153-1158.

Google Scholar