Topic model based structural Web services discovery

Chen Jiangfeng; Yu Jianjun

Volume 34 Issue 06

Jun. 2008

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Abstract

References

Journal of Beijing University of Aeronautics and Astronautics > 2008 > 34(06): 734-738.

ZHENG Yun, WEI Fa, JIANG Xinget al. Application of Micro Input-Output Theory to Enterprises' Logistics System[J]. Journal of Beijing University of Aeronautics and Astronautics, 2001, 27(5): 608-610. (in Chinese)

Citation:

Chen Jiangfeng, Yu Jianjun. Topic model based structural Web services discovery[J]. Journal of Beijing University of Aeronautics and Astronautics, 2008, 34(06): 734-738. (in Chinese)

Citation:

Chen Jiangfeng, Yu Jianjun. Topic model based structural Web services discovery[J]. Journal of Beijing University of Aeronautics and Astronautics, 2008, 34(06): 734-738. (in Chinese)

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Topic model based structural Web services discovery

Chen Jiangfeng¹,
Yu Jianjun²

1.
State Key Lab. Software Development Environment, Bejing University of Aeronautics and Astronautics, Beijing 100191, China
2. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China

Received Date: 04 Mar 2007
Publish Date: 30 Jun 2008

Abstract

Abstract

A structural Web services discovery approach based on a topic model was proposed. Each Web service would be modeled as a structural textual document using probabilistic model generated by latent dirichlet allocation (LDA). Every document could be seen as a multinomial random mixture of topics, and each topic had multinomial distribution over keywords, and thus a Web service retrieval based on the topic model was put forward. At the same time, the similar correlations in functionally structural Web services could be modeled as a directed acyclic graph (DAG) and be measured by using a gap-weighted n-spectrum kernel. Finally the experiment results show that several metrics for the classification and selection of services, such as success rate and executes efficiency, were improved via using our approach.
- topic model,
- Web services,
- n -spectrum kernel,
- service discovery

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References(1)

References

[1] Deerwester S, Dumais S, Furnas G.Indexing by latent semantic analysis[J]. Journal of the American Society for Information Science, 1990, 41:391-407 [2] Hofman T. Probabilistic latent semantic indexing Gey Fredric,Hearst Marti, Tong Richard. Proceedings of the 22nd Annual International ACM SIGIR Conference. New York:ACM,1999:50-57 [3] Blei D M, Ng A Y, Jordan M. Latent dirichlet allocation[J]. Journal of Machine Learning Research,2003,3(1):993-1022 [4] Li W, McCallum A. Pachinko allocation:DAG-structured mixture models of topic correlations Cohen William,Moore Andrew. Proceedings of the 23rd International Conference on Machine Learning. New York:ACM,2006:577-584 [5] Blei D M, Lafferty J D. Dynamic topic models Cohen William, Moore Andrew. Proceedings of the 23rd International Conference on Machine Learning. New York:ACM,2006:113-120 [6] Shawe-Taylor J,Cristianini N. Kernel methods for pattern analysis[M]. United Kingdom:Cambridge University Press,2004:344-372

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Topic model based structural Web services discovery