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.

这是一个标签的研究，标签分配给信息对象的用户，和“大众分类法”的分类系统，可以导致。 到了世纪，越来越多的信息被出版，很明显，有效的组织方法是需要的信息是可访问的。作为回应，像国会图书馆分类法和杜威分类法这样的分类方案被发明出来。整个信息传播系统包含明确的角色和分工：编辑决定出版什么，信息专业人员对出版的作品进行分类，大多数人只是消费结果。互联网颠覆了这种传统的方式。没有出版障碍，因此可以在网上获得数量级更多的信息，信息专业人员无法跟上。然而，在这种情况下出现了新的技术，特别是标记。任何用户都可以将标签与文档、电影或照片等项目相关联，标签用作检索的键。由于标签可以由任何用户创建，因此贡献的标签数量随社区的规模而变化：因此，标签在互联网规模上起作用。标记让用户代表他们自己的观点，这有助于检索。然而，标记是一项年轻的技术，具有重大的挑战和未满足的潜力。单个标签的质量通常很差，并且许多标签系统在全局上是不一致的。对使用中的标记系统的经验评价很少，与传统方法的正式比较也没有进行。标签应用主要局限于搜索。本项目旨在应对这些挑战。它将使人们对标记作为一种分类方法的优点和缺点有更牢固的科学认识。它将探索标记的潜力，使强大的应用程序超越信息检索。该项目包括三个主要的研究活动：（1）创建一组度量标准来量化分类结构的价值，并将这些度量标准用于标记系统与传统分类的正式和实证比较;（2）为计算代理和人设计混合主动交互技术，以检测、评估和解决标记系统中的问题;（3）开发新的基于标签的应用程序，让用户表达他们的偏好和导航复杂的信息空间。本研究将创建信息理论和基于使用的度量来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，让设计师预测，例如，有多少标签，每个项目需要有效的用户搜索。系统的成本效益比较的标记系统，以传统的专家分类，从而提供经验数据的辩论，其特点是激烈的猜测。一套混合主动交互技术和新应用的实用性和通用性将通过以下方式建立：（a）在多个平台上实现它们，（B）在仔细的现场实验中评估它们，提高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。更直接地说，该项目的技术将在四个在线社区中实施，分别面向电影观众、骑自行车的人、伦理研究人员和对政治感兴趣的公民。这些网站加起来有成千上万的用户，他们都将直接受益。许多学生将接受培训，学习多种研究方法，并获得与真实的在线社区的宝贵经验。最后，将根据开放源码许可证开发软件，并公布数据集，从而便利其他研究人员和网站开发人员的工作。