Topic model based structural Web services discovery
-
摘要: 提出了基于主题模型(topic model)的结构化Web服务发现机制.利用LDA(Latent Dirichlet Allocation)生成概率模型,将Web服务(Web service)建模为结构化文本文档.一个文档视作主题的概率分布,主题又由关键词的概率分布组成,从而提供基于主题的Web服务检索.同时,利用Web服务的结构化特性,将Web服务描述文档表示为有向无环图,利用n阶谱核测量Web服务文档的相似度,实现Web服务结构化信息的发现.通过实验分析对比,基于主题模型的结构化Web服务发现机制有效提高了Web服务发现的效率和精确率.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.
-
Key words:
- topic model /
- Web services /
- n -spectrum kernel /
- service discovery
-
[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 -
点击查看大图
计量
- 文章访问数: 3081
- HTML全文浏览量: 122
- PDF下载量: 1030
- 被引次数: 0
下载:
