Convergence Accelerator Phase I (RAISE): Linking the Open Knowledge Network to the Web with End-User Programming

NSF融合加速器支持以团队为基础的多学科努力，解决国家重要性的挑战，并在不久的将来显示出可交付成果的潜力。“汇聚加速器”第一阶段项目的更广泛影响和潜在社会效益是创建工具，使任何人（包括非程序员）都可以更容易地为开放知识网络做出贡献，这是一个非专有的共享知识基础设施，可以促进所有公开可用数据的搜索和查询。NSF的首要任务？s的2019年融合加速器计划是创建一个开放知识网络（OKN）和这个具体项目？美国的努力有可能在任何主题领域改善知识网络的发展。该项目将建立在该团队现有研究的基础上，以增强一种编程工具，使没有编程专业知识的人能够从网络上获取和使用大型、复杂的数据集，这样任何人都可以为开放的知识网络做出贡献，并帮助创建具有广泛价值的共享资源。该项目的动机是研究人员、决策者和公众需要获得更全面和最新的数据，首先关注与社会学、公共政策和经济学等广泛领域相关的数据。该项目旨在创建工具，使社会科学家能够丰富现有的OKN，其中包含由网站数据呈现的世界动态视图的政府数据集。该项目将生产编程工具，允许社会科学家（或任何研究人员）创建本体和链接数据集，而无需广泛的编程培训。要创建一个真正开放的OKN——可以由未经训练的消费者、数据提供者和其他非编码人员进行扩展——需要高度可学习的编程技术。该项目扩展了该团队在可学习编程领域的先前进展，围绕合并编程语言、人机交互和演示编程的尖端技术构建OKN接口。具体来说，使用该工具，该项目将开发一个用户将能够在网站上遇到一个目标数据集，该工具将演示如何收集数据的小样本，向用户展示如何用其本体（与其他术语和思想的连接）注释数据，然后根据用户的输入，该工具将编写程序从网站中提取下一个万亿个数据点并将其输入OKN。该团队还设想该工具可以建议其他可能适合链接的数据集，并且用户可以与该工具进行对话，以改进一个或多个链接脚本。研究团队将以他们现有的合作伙伴关系为基础，确保为一系列目标受众提供可用性最佳实践。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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 team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. The broader impact and potential societal benefit of this Convergence Accelerator Phase I project is to create tools that will make it easier for anyone, including non-programmers, to contribute to an open knowledge network, which is a nonproprietary, shared knowledge infrastructure that facilitates searches and queries of all publicly available data. An overarching thrust of NSF?s 2019 Convergence Accelerator program is to create an open knowledge network (OKN) and this specific project?s efforts have the potential to improve the development of knowledge networks in any topical domain. The project effort will build upon the team's existing research to enhance a programming tool that will allow people without programming expertise to acquire and use large, complex datasets from the web, so that anyone can contribute to an open knowledge network and help create a shared resource of broad value. The motivation for the project is the need for researchers, decision-makers, and the public to have access to more thorough and current data, focusing first on data relevant to the broad fields of sociology, public policy, and economics. The project seeks to create tools so that social scientists can enrich an existing OKN containing government datasets with the dynamic view of the world presented by website data. This project will produce programming tools that allow social scientists (or any researcher) to create ontologies and link datasets without extensive programming training. To create a truly open OKN - that can be extended by untrained consumers, data providers, and other non-coders - requires highly learnable programming techniques. This project expands the team's prior advances in the field of learnable programming to build an OKN interface around cutting-edge technologies that merge programming languages, human-computer interaction, and programming-by-demonstration. Concretely, using the tool this project will develop a user will be able to encounter a target dataset on a website, the tool will demonstrate how to collect a small sample of the data, show the user how to annotate the data with its ontology (connections to other terms and ideas), then based on the user's input, the tool will write the program to extract the next trillion data points from the website and enter them into the OKN. The team also envisions that the tool can then suggest other datasets that may be appropriate to link, and the user can enter into a conversation with the tool to refine one or more linked scripts. The research team will build on their existing partnerships to ensure usability best practices for a range of target audiences.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.