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摘要翻译：
许多社交网站允许用户发布内容并使用描述性元数据进行注释。除了平面标记，一些社交网站最近开始允许用户分层组织他们的内容和元数据。例如，社交照片共享网站Flickr允许用户将相关照片分组为一组，将相关照片分组为一组。类似地，社交书签网站del.icio.us允许用户将相关标签分组到包中。尽管站点本身对如何使用这些层次结构没有任何限制，但个人通常使用它们来捕获概念之间的关系，最常见的是更广泛/更狭义的关系。对具有等级关系的内容的集体注释可能导致一个涌现的分类系统，称为大众分类学。虽然一些研究人员已经探索使用标签作为学习大众分类法的证据，但我们相信上面描述的层次关系为这项任务提供了高质量的证据来源。我们提出了一种简单的方法，将许多不同的Flickr用户创建的浅层次结构聚合到一个公共的Folksonomy中。我们的方法使用统计来确定是否应该保留或丢弃特定的关系。然后这些关系被编织成更大的层次结构。虽然我们还没有对该方法进行详细的定量评估，但它看起来非常有希望，因为它生成了非常合理的、非平凡的层次结构。
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英文标题：
《Constructing Folksonomies from User-specified Relations on Flickr》
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作者：
Anon Plangprasopchok and Kristina Lerman
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最新提交年份：
2008
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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 publish content and annotate with descriptive metadata. In addition to flat tags, some social Web sites have recently began to allow users to organize their content and metadata hierarchically. The social photosharing site Flickr, for example, allows users to group related photos in sets, and related sets in collections. The social bookmarking site Del.icio.us similarly lets users group related tags into bundles. Although the sites themselves don't impose any constraints on how these hierarchies are used, individuals generally use them to capture relationships between concepts, most commonly the broader/narrower relations. Collective annotation of content with hierarchical relations may lead to an emergent classification system, called a folksonomy. While some researchers have explored using tags as evidence for learning folksonomies, we believe that hierarchical relations described above offer a high-quality source of evidence for this task. We propose a simple approach to aggregate shallow hierarchies created by many distinct Flickr users into a common folksonomy. Our approach uses statistics to determine if a particular relation should be retained or discarded. The relations are then woven together into larger hierarchies. Although we have not carried out a detailed quantitative evaluation of the approach, it looks very promising since it generates very reasonable, non-trivial hierarchies.
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PDF链接：
https://arxiv.org/pdf/0805.3747
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