A1: Knowledge Network Development Infrastructure with Application to COVID-19 Science and Economics

国家科学基金会融合加速器支持以使用为灵感、基于团队的多学科努力，以应对国家重要性的挑战，并将在不久的将来产生有价值的社会成果。该项目的目标是为高效构建知识网络和应用程序构建基础设施，以及用描述新冠肺炎科学和经济学的具体知识网络展示该体系。在短期内，这项工作将产生高精度的数据资源，这将有助于科学家和政策制定者解决病毒及其经济影响。样本目标包括使医学研究人员能够快速确定相关的候选药物，以及使政策制定者能够快速评估一项新法律的可能影响。该项目将创建编程工具，使知识网络及其应用程序的构建成本大大降低。这一方案编制工具的基础设施将促进创建一套庞大和新颖的信息工具，并将大大扩大能够参与创建知识网络资源的人员队伍。由于知识网络将独特的数据分析能力与整个万维网的专题广度结合在一起，知识工具的潜在增长是非常巨大的，具有潜在的变革性。该项目包括与一批强大的非学术和学术合作伙伴建立伙伴关系。这个融合研究团队将把他们在数据管理、人工智能、编程语言、与新冠肺炎相关的生物医学主题和经济学方面的多学科专业知识，与Track A第二阶段队列资助的项目中代表的其他领域相结合。建立这种知识规划基础设施和具体的知识网络将需要解决若干技术挑战。首先是一个智能的“知识汇编层”，它使有用但快速变化的知识网络看起来足够稳定，以便程序员在编写可靠代码时使用它们。第二是建立一种机制，在组织内部和组织之间透明地共享知识资源和调试信息。第三种是通过用户软件的自动检测收集知识来源元数据的方法--详细说明每个单独的数据元素是如何创建的。最后一个挑战是创建从文档中获取知识的系统，该系统可以产生高精度的知识网络，几乎没有明确的人类监督。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future.The goal of this project is to build infrastructure for efficient construction of knowledge networks and applications, as well as to demonstrate the system with concrete knowledge networks that describe COVID-19 science and economics. In the short term, this work will lead to high accuracy data resources that will be useful to scientists and policy makers in addressing the virus and its economic impact. Sample goals include enabling a medical researcher to quickly identify relevant candidate drugs, and a policy maker to quickly evaluate the likely impacts of a novel law. The project will create programming tools that will make knowledge networks and their applications far less expensive to build. This infrastructure of programming tools will facilitate the creation of a large and novel set of informational tools and will also significantly expand the set of people who can participate in creating knowledge network resources. Because knowledge networks combine unique data analysis qualities with the topical breadth of the entire World Wide Web, the potential growth of knowledge tools is very large and potentially transformative. This project includes partnerships with a strong set of non-academic and academic partners. This convergence research team will integrate their multidisciplinary expertise in data management, artificial intelligence, programming languages, biomedical topics relevant to COVID-19, and economics, with the other domains represented in the projects funded in the Track A Phase II cohort. Creating this knowledge programming infrastructure and concrete knowledge networks will require solving several technical challenges. The first is an intelligent “knowledge compilation layer” that makes useful but rapidly-changing knowledge networks appear to be stable enough for programmers to use them when writing reliable code. The second is the creation of a mechanism for transparently sharing knowledge resources and debugging information within and across organizations. The third is a method for collecting knowledge provenance metadata — details about how every individual data element was created — via automatic instrumentation of user software. A last challenge is the creation of knowledge-from-document systems that can produce high accuracy knowledge networks with very little explicit human oversight.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.