Convergence Accelerator Phase I (RAISE): Simultaneous Knowledge Network Programming and Extraction

NSF融合加速器支持基于团队的多学科努力，以应对国家重要性的挑战，并在不久的将来显示出交付成果的潜力。这一融合加速器第一阶段项目的更广泛影响和潜在的社会效益将包括更好地利用和发展知识网络。今天的知识网络，例如维基数据，包括关于非常广泛的主题的高质量的结构化信息。知识网络使许多新的、引人注目的应用成为可能，例如结构化搜索引擎结果和语音助手。不幸的是，今天的知识网络和应用程序的构建已经非常困难和昂贵，这使得为新的主题创建它们变得极其繁重。该项目将利用研究团队在数据管理、人工智能和经济学方面的专业知识，创建软件和数据的组合，使新型知识网络系统的生产变得非常容易。第一项努力将是一个以经济学为重点的综合知识网络和工具系统，它有可能极大地提高执行更高质量的经济计量和分析的便利性。这一知识网络工具可以改善面向经济的研究工作，这将有利于国家的繁荣。然而，这项工作的更大价值将是一种工具，使任何主题的知识网络能够更容易地开发，编程专业知识更少。尽管知识网络被认为是未来数据发现的关键，但知识网络驱动的应用程序通常不是使用可复制的系统开发的。这个项目将开始建立一个知识应用开发系统，使知识应用更容易编写，使现有知识网络更容易改进，使全新的知识网络更容易构建。该小组的努力是基于他们开发的一种新颖而极其简洁的编程形式，这种形式允许同时编程和提取相关信息，以促进知识网络的建立。拟议的同时规划和提取系统将有助于构建知识网络，但也将提高知识网络的数据质量，方法是对通常用于生产知识网络的信息提取管道提供额外的薄弱监督。该系统将在真实数据和经济学领域的用户上进行测试。然而，这些方法和工具将不是特定于主题的，而是应该广泛适用于许多主题领域的知识网络。这项工作将产生研究以及实用的可下载软件、数据集和应用程序。该奖项反映了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 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 will include better use and growth of knowledge networks. Today's knowledge networks, for example Wikidata, include high-quality structured information about a very wide range of topics. Knowledge networks make many new and compelling applications possible, such as structured search engine results and voice assistants. Unfortunately, today's knowledge networks and applications have been very difficult and expensive to construct, making it extremely burdensome to create them for novel topics. This project will take advantage of the research team's expertise in data management, artificial intelligence, and economics to create a combination of software and data that should make novel knowledge network systems dramatically easier to produce. The first effort will be an Economics-focused integrated knowledge network-and-tool system, which has the potential to dramatically improve the ease of performing higher-quality economic measurement and analysis. This knowledge network tool can improve economic-oriented research efforts that will benefit national prosperity. However, the even greater value of the effort will be a tool that allows knowledge networks on any topic to be developed more easily and with less programming expertise. Although knowledge networks are thought to be key to future data-enabled discovery, knowledge network-driven applications have generally not been developed using a reproducible system. This project will begin to build a knowledge application development system that should make knowledge applications easier to write, existing knowledge networks easier to improve, and entirely novel knowledge networks easier to construct. The team's effort is based on a novel and extremely succinct form of programming that they have developed that allows simultaneous programming and extraction of relevant information to contribute to a knowledge network. The proposed simultaneous programming and extraction system will help construct knowledge networks, but will also improve knowledge network data quality, by providing additional weak supervision for the information extraction pipelines that are commonly used to produce the networks. The system will be tested on real data and users in the Economics domain. However, the methods and tools will not be topic-specific, but rather should be widely applicable to knowledge networks in many topical domains. This work will generate research as well as practical downloadable software, datasets, and applications.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.