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

机译:使用多实例学习的Web服务的基于用户反馈的细化

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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.
机译:重用现有WSDL指定组件的过程中的一个关键步骤是发现可能相关的Web服务。基于传统的基于Web服务检索通常可以实现良好的召回,但更糟糕的精确度,因为某些语义相关的Web服务实际上并不像它们无法提供合适的接口。在本文中,我们介绍了一个交互式Web服务检索机制,以改进基于类别的检索中的粗略检索结果。在细化中,从多实例视图研究了关于操作规范结构的Web服务的签名匹配。详细地,每个Web服务在多实例学习中表示为袋子,而该Web服务中的每个操作被视为实例。该表示在于,如果该Web服务提供的至少一个操作是有用的,则用户将服务视为有用的服务。实验结果表明,我们的方法可以显着提高检索性能:在两轮用户相关反馈后,它可以平均获得83%的精度。

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