User Feedback-Based Refinement for Web Services Retrieval using Multiple Instance Learning

2006 IEEE International Conference on Web Services (ICWS'06) Pub Date : 2006-09-18 DOI:10.1109/ICWS.2006.142

Yanzhen Zou, Liangjie Zhang, Lu Zhang, Bing Xie, Hong Mei

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引用次数: 3

Abstract

A critical step in the process of reusing existing WSDL-specified components is the discovery of potentially relevant Web services. Traditional category based Web service retrieval usually can achieve good recall but worse precision because some semantically relevant Web services are not actually relevant as they cannot provide suitable interfaces. In this paper, we present an interactive Web services retrieval mechanism to refine the coarse retrieval results set in category based retrieval. In the refinement, the signature matching of Web services that concerning the structure of operation specifications is investigated from a multi-instances view. In detail, each Web service is represented as a bag in multiple instance learning, while each operation in this Web service is regarded as an instance. This representation lies in that a user regards a service as useful if at least one operation provided by this Web service is useful. Experimental results show that our approach can improve the retrieval performance significantly: It can gain 83% precision in average after two rounds of user relevance feedback

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基于用户反馈的多实例学习Web服务检索优化

重用现有wsdl指定组件过程中的一个关键步骤是发现潜在的相关Web服务。传统的基于类别的Web服务检索通常可以实现良好的召回，但精度较差，因为一些语义相关的Web服务实际上并不相关，因为它们不能提供合适的接口。本文提出了一种交互式Web服务检索机制，对基于类别的检索中粗糙的检索结果集进行细化。在细化中，从多实例的角度研究了涉及操作规范结构的Web服务的签名匹配问题。详细地说，在多实例学习中，每个Web服务都表示为一个包，而此Web服务中的每个操作都被视为一个实例。这种表示方式在于，如果该Web服务提供的至少一个操作是有用的，则用户认为该服务是有用的。实验结果表明，该方法可以显著提高检索性能:经过两轮用户相关性反馈后，平均准确率可提高83%

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来源期刊

2006 IEEE International Conference on Web Services (ICWS'06)

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