利用社会标注实现资源自动发现
Exploiting Social Annotation for Automatic Resource Discovery
摘要：
需要访问信息资源的信息集成应用程序（如中介或mashup）目前依赖于用户手动发现它们并将它们集成到应用程序中。手动资源发现是一个缓慢的过程，需要用户筛选通过基于关键字的搜索获得的结果。虽然搜索方法已经发展到包括来自文件内容的证据、其元数据以及参考网页的内容和链接结构，但它们仍然没有充分涵盖信息源-通常被称为“隐藏的网络”-这些信息源根据查询动态生成文件。最近流行的社会书签网站，它允许用户注释和共享有关各种信息源的元数据，为资源发现提供了丰富的证据。在本文中，我们描述了一个概率模型的用户注释过程中的社会书签系统del.icio.us。然后，我们使用该模型来自动查找与特定信息域相关的资源。我们的实验结果从\emph{del.icio.us}获得的数据显示这种方法作为一个有前途的方法，帮助自动化的资源发现任务。
英文摘要：
Information integration applications, such as mediators or mashups, that require access to information resources currently rely on users manually discovering and integrating them in the application. Manual resource discovery is a slow process, requiring the user to sift through results obtained via keyword-based search. Although search methods have advanced to include evidence from document contents, its metadata and the contents and link structure of the referring pages, they still do not adequately cover information sources -- often called ``the hidden Web''-- that dynamically generate documents in response to a query. The recently popular social bookmarking sites, which allow users to annotate and share metadata about various information sources, provide rich evidence for resource discovery. In this paper, we describe a probabilistic model of the user annotation process in a social bookmarking system del.icio.us. We then use the model to automatically find resources relevant to a particular information domain. Our experimental results on data obtained from \emph{del.icio.us} show this approach as a promising method for helping automate the resource discovery task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
