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首页> 外文会议>2013 International Joint Conference on Awareness Science and Technology and Ubi-Media Computing >Web service filtering and visualization with context aware similarity to bootstrap clustering
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Web service filtering and visualization with context aware similarity to bootstrap clustering

机译：Web服务过滤和可视化，具有与引导群集相似的上下文感知的相似性

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摘要

Web service clustering is an efficient approach to address some challenges in service computing area such as discovering and recommending. To cluster the Web services, we need to filter the similar services. Key operation of filtering process is measuring the similarity of services. There are several methods used in current similarity calculation approaches such as keyword, information retrieval, ontology and hybrid methods. However, these approaches do not consider the context when measuring the similarity. So these approaches failed to capture the semantic of terms, which exist under a certain domain. In this paper, we propose context aware similarity method, which uses search results from search engines and support vector machine. Then, we apply Associated Keyword Space (ASKS) algorithm which is effective for noisy data and projected results from a three-dimensional (3D) sphere to a two dimensional (2D) spherical surface for 2D visualization to filter the services. Experimental results show our filtering approach is able to filter services based on domain and plot the result on sphere. Also our approach performs better than the existing approaches. Further, our approach aids to search Web services by visualization of the service data on a spherical surface.
机译：Web服务集群是一种有效的方法，可以解决服务计算领域中的一些挑战，例如发现和推荐。要群集Web服务，我们需要过滤相似的服务。筛选过程的关键操作是衡量服务的相似性。当前的相似度计算方法中使用了几种方法，例如关键字，信息检索，本体和混合方法。但是，这些方法在测量相似性时不考虑上下文。因此，这些方法无法捕获存在于特定域中的术语的语义。在本文中，我们提出了一种上下文相关的相似性方法，该方法使用来自搜索引擎和支持向量机的搜索结果。然后，我们应用有效处理嘈杂数据的关联关键字空间（ASKS）算法，并将结果从三维（3D）球体投影到二维（2D）球形表面以进行2D可视化以过滤服务。实验结果表明，我们的过滤方法能够基于域过滤服务并将结果绘制在球上。同样，我们的方法比现有方法具有更好的性能。此外，我们的方法通过在球形表面上可视化服务数据来帮助搜索Web服务。

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