在森林中生长一棵树：通过整合构建大众分类学 结构化元数据

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摘要翻译：
许多社交网站允许用户使用描述性元数据（如标记）注释内容，最近还允许用户分层组织内容。这些类型的结构化元数据为了解社区如何组织知识提供了有价值的证据。例如，我们可以将许多个人层次结构聚合到一个公共分类法中，也称为folksonomy，这将帮助用户可视化和浏览社交内容，并帮助他们组织自己的内容。然而，从社会元数据中学习带来了一些挑战，因为它是稀疏的、肤浅的、模棱两可的、嘈杂的和不一致的。我们描述了一种基于关系聚类的folksonomy学习方法，该方法利用包含在个人层次结构中的结构化元数据。我们的方法使用相似的层次结构和标记统计信息对其进行聚类，然后逐步地将它们编织到一个更深、更忙碌的树中。我们使用从照片共享网站Flickr中提取的社会元数据研究了folksonomy学习，并证明了所提出的方法解决了这些挑战。此外，与以前的工作相比，该方法产生了更大，更准确的大众分类法，此外，规模更好。
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英文标题：
《Growing a Tree in the Forest: Constructing Folksonomies by Integrating
  Structured Metadata》
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作者：
Anon Plangprasopchok, Kristina Lerman, Lise Getoor
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最新提交年份：
2010
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  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.
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PDF链接：
https://arxiv.org/pdf/1005.5114

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关键词：结构化 Hierarchical Presentation Intelligence Constructing also 学习 metadata content approach