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，以应对国家重要性的挑战，并在不久的将来展示可交付成果的潜力。这个融合加速器第一阶段项目的更广泛的影响和潜在的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的知识网络做出贡献，这是一个非专有的共享知识基础设施，便于搜索和查询所有公开可用的数据。NSF的一个主要推力？的2019年融合加速器计划是创建一个开放的知识网络（OKN）和这个具体的项目？教科文组织的努力有可能改善任何专题领域知识网络的发展。该项目的工作将建立在团队现有的研究基础上，以增强编程工具，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，以便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，使社会科学家可以丰富现有的OKN包含政府数据集与网站数据呈现的动态世界观。该项目将产生编程工具，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 -可以由未经培训的消费者，数据提供者和其他非编码人员扩展-需要高度可学习的编程技术。该项目扩展了团队在可学习编程领域的先前进展，围绕融合编程语言，人机交互和演示编程的尖端技术构建OKN界面。具体地说，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一个小样本的数据，向用户展示如何注释数据与其本体（与其他术语和想法的连接），然后基于用户的输入，该工具将编写程序从网站中提取下一万亿个数据点，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，用户可以与该工具进行对话，以改进一个或多个链接的脚本。该研究团队将建立在现有的合作伙伴关系，以确保可用性的最佳做法，为一系列的目标受众。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的知识价值和更广泛的影响审查标准的支持。

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.