CDI-Type II: Collaborative Research: Bibliographic Knowledge Network

本项目将开发一套工具和服务，鼓励在不同规模的科学团体中建立虚拟组织，例如会议小组和部门研究小组，并允许这些组织从各种输入流中过滤出有关文件，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。文献计量学分析、机器学习和统计可视化方法将被应用于帮助探索和理解不同规模的书目集合，例如一个研究小组制作的所有作品，或发表在期刊上的所有作品，或一个领域的所有作品。这将提供一个交互式环境，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基于协同过滤和书目数据语义分析的文章推荐服务，最初是为概率和统计研究人员提供的。研究还将为数学领域的作者提供足够的创作工具，以便轻松地以latex， bibtex和或类似格式创建高度结构化，机器可读的文档，然后可以轻松地在百科全书式编译中汇总和相互关联，然后进行机器学习和统计分析，以提供这些领域景观的高级概述。在统计、数学和相关领域，包括社会科学，我们期望通过该项目创建和公开的关于作者、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，加速这些子学科之间的知识传播，推动这些领域的发展。就信息科学而言，该项目应促进对协同生产和在线书目信息增强的理解，利用以视觉刺激方式呈现的灵活的相似性度量来吸引兴趣并鼓励研究人员扩展他们的搜索参数。该建议解决了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引导学生和研究人员），以及维护问题（如何为个人和组织提供激励，以提高公共可访问知识的质量）。为了解决这些问题，我们建议将目前以数字格式提供的大量异构数据逐步提炼成一个公开可导航的网站网络，即书目知识网络（BKN），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。每个参与的站点通常被设计为特定知识领域的研究人员、教师和学生的指南，并由致力于该领域的虚拟组织维护。BKN将通过软件开发来创建，该软件使大型集合（主要是小型和分布式组织）能够轻松地标记、选择、维护和注释结构化科学内容集合。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思想联系起来。将应用机器学习的方法来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可以在学术界之外使用，并向任何有互联网连接的人提供组织良好的高质量信息。该系统的说明性组成部分将吸引各种背景的人从事科学事业，并将允许各级学生接触将引导他们进入更高水平的材料。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系统、维基百科、开放期刊系统和免费学术搜索服务。

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.