CDI Type II: Collaborative Research: Bibliographic Knowledge Network

CDI II 型：协作研究：书目知识网络

• 批准号: 0835500
• 负责人: Gary King
• 金额: $ 21.71万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-10-01 至 2011-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0835500&HistoricalAwards=false
• 关键词: CDI; Type; II; Collaborative; Research

This project will develop a suite of tools and services to encourage formation of virtual organizations in scientific communities of various sizes, such as conference groups and departmental research groups, and allow such organizations to filter out relevant documents from various input streams, select and enhance the quality of bibliographic data associated with the organization, and attract researchers to contribute to activity of the organization. Methods of bibliometric analysis, machine learning and statistical visualization will be applied to assist the exploration and understanding of bibliographic collections of various sizes, for example all work produced by a research group, or published in a journal, or all work in a field. This will provide an interactive environment which allows the researcher to move beyond static summaries to dynamically explore the environment in which an article of interest exists. In particular, methods of machine-learning will be applied to to build an article recommendation service, based on collaborative filtering and on semantic analysis of bibliographic data, initially for researchers in probability and statistics. Research will also be done to provide adequate authoring tools for authors in mathematical fields to easily create highly structured, machine-readable documents in latex, bibtex and or similar formats, which can then be easily aggregated and interlinked in encyclopedic compilations, and then subjected to machine-learning and statistical analysis to provide high-level overviews of the landscape of these fields. In statistics, mathematics and related fields, including social science, we expect the networks of information about authors, publications, problems and datasets that will be created and exposed through this project should advance these fields by revealing hidden connections among different sub-disciplines, and accelerating the transmission of knowledge across these sub-disciplines. With respect to information science, the project should advance understanding of the collaborative production and enhancement of bibliographic information online, leveraging flexible similarity metrics presented in a visually stimulating way to draw interest and encourage the researcher to expand their search parameters. This proposal addresses three fundamental problems of knowledge management: the compartmentalization problem (how to break down barriers which separate disciplines), the navigation problem (how to guide students and researchers within and between disciplines), and the maintenance problem (how to provide incentives for individuals and organizations to improve the quality of publicly accessible knowledge). It is proposed to solve these problems by gradually distilling the wealth of heterogeneous data now available in digital formats into an openly navigable network of websites, the Bibliographic Knowledge Network (BKN), each node of which is a website dedicated to a specific topic or field of knowledge. Each participating site will typically be designed as a guide for researchers, teachers, and students in a particular field of knowledge, and maintained by a Virtual Organization with a commitment to that field. The BKN will be created through the development of software which makes it easy for a large collection of mostly small and distributed organizations to brand, select, maintain, and annotate collections of structured scientific content. That content will be made available in machine-readable formats, to allow connections between ideas in different disciplines to be made using methods of machine learning. Methods of machine learning will be applied to provide article recommendation services based on both collaborative filtering and semantic analysis of documents. The collective knowledge system emerging from this project will be available beyond the walls of academia, and provide well-organized high quality information to anyone with an Internet connection. The expository components of the system will attract people from all backgrounds to pursue scientific careers, and will allow students at all levels to encounter materials which will lead them to higher levels. The system will add great value to other Open Access initiatives, including the system of interoperable digital repositories, Wikipedia, Open Journal Systems, and free academic search services.

该项目将开发一套工具和服务，以鼓励在各种规模的科学界成立虚拟组织，如会议小组和部门研究小组，并使这些组织能够从各种输入流中筛选出相关文件，选择和提高与该组织有关的书目数据的质量，并吸引研究人员为该组织的活动作出贡献。将应用文献计量分析、机器学习和统计可视化方法来帮助探索和理解各种规模的书目收藏，例如研究小组制作的所有作品，或在期刊上发表的所有作品，或某个领域的所有作品。这将提供一个交互式的环境，使研究人员能够超越静态摘要，动态地探索感兴趣的文章存在的环境。特别是，机器学习的方法将被应用于建立一个文章推荐服务，基于协同过滤和书目数据的语义分析，最初为概率和统计的研究人员。还将进行研究，为数学领域的作者提供适当的创作工具，以便以latex、bibtex和/或类似格式轻松创建高度结构化的机器可读文件，然后可以轻松地将这些文件汇总和链接到百科全书汇编中，然后进行机器学习和统计分析，以提供这些领域的高级概览。在统计学、数学和相关领域，包括社会科学，我们希望通过该项目创建和暴露的关于作者、出版物、问题和数据集的信息网络，通过揭示不同子学科之间的隐藏联系，并加速知识在这些子学科之间的传播，推动这些领域的发展。在信息科学方面，该项目应促进对在线书目信息的协作制作和增强的理解，利用以视觉刺激的方式呈现的灵活的相似性度量来引起兴趣并鼓励研究人员扩大其搜索参数。这一建议涉及知识管理的三个基本问题：划分问题（如何打破学科之间的障碍）、导航问题（如何在学科内部和学科之间指导学生和研究人员）和维护问题（如何激励个人和组织提高可公开获取的知识的质量）。建议通过逐步将目前以数字格式提供的丰富的异构数据提炼成一个开放的可导航网站网络，即书目知识网络（BKN）来解决这些问题，其中每个节点都是一个专门针对特定主题或知识领域的网站。每个参与网站通常都是为特定知识领域的研究人员、教师和学生设计的指南，并由致力于该领域的虚拟组织维护。BKN将通过开发软件来创建，该软件使大多数小型和分布式组织的大型集合易于品牌化，选择，维护和注释结构化科学内容的集合。这些内容将以机器可读的格式提供，以便使用机器学习方法将不同学科的想法联系起来。机器学习的方法将被应用于提供基于协同过滤和文档语义分析的文章推荐服务。从这个项目中产生的集体知识系统将超越学术界的围墙，并提供组织良好的高质量的信息，任何人与互联网连接。该系统的临时组成部分将吸引来自各种背景的人从事科学事业，并将使各级学生接触到将使他们达到更高水平的材料。该系统将为其他开放获取计划增加巨大价值，包括可互操作的数字存储库系统、维基百科、开放期刊系统和免费学术搜索服务。