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生长在森林的一棵树上:通过整合结构化元数据进行构造分类

Growing a Tree in the Forest: Constructing Folksonomies by Integrating Structured Metadata
课程网址: http://videolectures.net/kdd2010_plangprasopchok_gtf/  
主讲教师: Anon Plangprasopchok
开课单位: 南加州大学
开课时间: 2010-10-01
课程语种: 英语
中文简介:
许多社交网站允许用户使用描述性元数据(如标记)对内容进行注释,最近还允许用户按层次组织内容。这些类型的结构化元数据为学习社区如何组织知识提供了宝贵的证据。例如,我们可以将许多个人层次结构聚合到一个通用的分类法中,也称为民俗分类法,它将帮助用户可视化和浏览社会内容,并帮助他们组织自己的内容。然而,从社会元数据中学习会带来一些挑战,因为它是稀疏的、浅薄的、模糊的、嘈杂的和不一致的。我们描述了一种基于关系聚类的民俗学学习方法,它利用了包含在个人层次结构中的结构化元数据。我们的方法使用它们的结构和标记统计信息来集群类似的层次结构,然后逐步地将它们编织成一个更深、更浓密的树。我们使用从照片共享网站Flickr中提取的社会元数据来研究民声学学习,并证明所提出的方法解决了这些挑战。此外,与以前的工作相比,这种方法产生了更大、更准确的民间分类法,而且,规模更好。
课程简介: Many social Web sites allow users to annotate the content with descriptive metadata, such as tags, and more recently to organize content hierarchically. These types of structured metadata provide valuable evidence for learning how a community organizes knowledge. For instance, we can aggregate many personal hierarchies into a common taxonomy, also known as a folksonomy, that will aid users in visualizing and browsing social content, and also to help them in organizing their own content. However, learning from social metadata presents several challenges, since it is sparse, shallow, ambiguous, noisy, and inconsistent. We describe an approach to folksonomy learning based on relational clustering, which exploits structured metadata contained in personal hierarchies. Our approach clusters similar hierarchies using their structure and tag statistics, then incrementally weaves them into a deeper, bushier tree. We study folksonomy learning using social metadata extracted from the photo-sharing site Flickr, and demonstrate that the proposed approach addresses the challenges. Moreover, comparing to previous work, the approach produces larger, more accurate folksonomies, and in addition, scales better.
关 键 词: 社交网站; 元数据; 大众分类法; 关联聚类分类学习
课程来源: 视频讲座网
最后编审: 2019-12-24:lxf
阅读次数: 41