HCC: Medium: Collaborative Research: Guiding Folksonomy Development to Enable Novel Tagging Applications

HCC：媒介：协作研究：指导 Folksonomy 开发以实现新颖的标签应用

• 批准号: 0964695
• 负责人: Loren Terveen
• 金额: $ 94.98万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-04-15 至 2016-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0964695&HistoricalAwards=false
• 关键词: HCC; Medium; Collaborative; Research; Guiding

This is a study of tagging, the assignment of labels to information objects by users, and the "folksonomy" categorization systems that can result. By the 19th Century, increasing amounts of information were being published, and it was clear that efficient methods of organization were needed for the information to be accessible. In response, categorization schemes like the Library of Congress Classification and the Dewey Decimal System were invented. The overall information dissemination system contained clear roles and divisions of labor: editors decided what got published, information professionals categorized published works, and most people simply consumed the results. The Internet has toppled this traditional approach. There is no publication barrier, so orders of magnitude more information is available online and information professionals cannot keep up. However, new technologies have arisen that work in this context, notably tagging. Any user can associate tags with items such as documents, movies, or photos, and the tags serve as keys for retrieval. Since tags can be created by any user, the number of tags contributed scales with a community's size: thus, tagging works at Internet scale. Tagging lets users represent their own perspectives, which aids retrieval.However, tagging is a young technology, with significant challenges and unmet potential. Individual tags are often of poor quality, and many tagging systems are globally incoherent. Empirical evaluations of tagging systems in use are few, and formal comparisons to traditional approaches have not been done. Tagging applications have been limited mainly to search. This project addresses these challenges. It will develop a firmer scientific understanding of the strengths and weaknesses of tagging as a categorization method. It will explore the potential of tagging to enable powerful applications beyond information retrieval. The project consists of three main research activities: (1) Creating a set of metrics to quantify the value of a categorization structure; using these metrics in formal and empirical comparisons of tagging systems to traditional categorizations; (2) Designing mixed-initiative interaction techniques for computational agents and people to detect, evaluate and resolve problems in tagging systems; (3) Developing novel tag-based applications for users to express their preferences and navigate complex information spaces.This research will create both information-theoretic and usage-based metrics to measure the value of a categorization structure. Studies will be done to show relations between the two types of metric, letting designers predict, for example, how many tags per item are required for effective user search. Systematic cost-benefit comparisons of tagging systems to traditional expert categorizations will be done, thus providing empirical data to a debate that has been characterized by heated conjecture. The utility and generality of a set of mixed-initiative interaction techniques and novel applications will be established by (a) implementing them in multiple platforms, and (b) evaluating them in careful field experiments.Improving the effectiveness of tagging will help millions of users find the information, products, and services they seek. More directly, the techniques of this project will be implemented in four working online communities, for movie viewers, cyclists, ethics researchers, and politically interested citizens. Collectively these sites have tens of thousands of users, all of whom will benefit directly. Many students will be trained, learning multiple research methods and gaining valuable experience with real online communities. Finally, the software will be developed under an open source license and datasets will be published, thus facilitating other researchers and web site developers in their work.

这是一项针对标签的研究，用户将标签分配给信息对象的标签以及可能导致的“ folksonomy”分类系统。 到19世纪，已经发布了越来越多的信息，很明显，需要有效的组织方法才能访问该信息。作为回应，发明了诸如国会分类图书馆和杜威十进制系统之类的分类方案。整体信息传播系统包含明确的劳动角色和分区：编辑决定了出版的内容，信息专业人士对已发表的作品进行了分类，大多数人只是消耗了结果。互联网已经推翻了这种传统方法。没有出版障碍，因此可以在线提供更多信息，并且信息专业人士无法跟上。但是，在这种情况下起作用的新技术，尤其是标记。任何用户都可以将标签与文档，电影或照片等项目相关联，并且标签是检索的钥匙。由于标签可以由任何用户创建，因此标签的数量贡献了社区大小的尺度：因此，标记在Internet规模上工作。标记使用户代表自己的观点，这有助于检索。单个标签通常质量较差，许多标记系统在全球范围内都是不一致的。使用标签系统的经验评估很少，与传统方法的正式比较尚未进行。标记应用程序主要限制在搜索中。该项目解决了这些挑战。它将对标记作为一种分类方法的优势和缺点有更牢固的科学理解。它将探索标签的潜力，以启用超出信息检索的强大应用程序。该项目由三个主要研究活动组成：（1）创建一组指标来量化分类结构的价值；在标记系统的正式和经验比较中使用这些指标与传统分类； （2）为计算代理和人员设计混合互动技术，以检测，评估和解决标记系统中的问题； （3）开发基于标签的新型应用程序，以表达其偏好并导航复杂的信息空间。这项研究将创建信息理论和基于用法的指标，以衡量分类结构的价值。将进行研究以显示两种类型的度量之间的关系，例如，设计师预测，有效的用户搜索需要每个项目多少标签。将进行标记系统与传统专家分类的系统性成本效益比较，从而为以加热猜想为特征的辩论提供经验数据。 （a）在多个平台中实施它们，以及（b）在仔细的现场实验中对其进行评估，将建立一组混合定位互动技术和新颖应用的效用和通用性。提出标记的有效性将帮助数百万用户找到他们所寻求的信息，产品和服务。更直接地，该项目的技术将在四个工作的在线社区中实施，用于电影观众，骑自行车的人，道德研究人员和政治上感兴趣的公民。这些站点共有成千上万的用户，所有这些用户都将直接受益。许多学生将接受培训，学习多种研究方法，并获得真正的在线社区的宝贵经验。最后，该软件将根据开源许可开发，并将发布数据集，从而促进其他研究人员和网站开发人员的工作。