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

融合加速器第一阶段（RAISE）：同步知识网络编程和提取

• 批准号: 1936940
• 负责人: Michael Cafarella
• 金额: $ 100万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1936940&HistoricalAwards=false
• 关键词: Convergence; Accelerator; Phase; RAISE; Simultaneous

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.

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结构化信息。知识网络使许多新的和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已经非常困难和昂贵的建设，使其极其繁重的创造新的主题。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，创建一个软件和数据的组合，使新的知识网络系统更容易生产。第一项工作将是建立一个以经济学为重点的综合知识网络和工具系统，该系统有可能极大地方便进行更高质量的经济计量和分析。这一知识网络工具可以改善面向经济的研究工作，从而有利于国家繁荣。然而，这项工作的更大价值将是一个工具，使任何专题的知识网络都能更容易地建立起来，而不需要太多的方案拟订专门知识。虽然知识网络被认为是未来数据驱动发现的关键，但知识网络驱动的应用程序通常没有使用可复制的系统开发。本项目将开始建立一个知识应用开发系统，使知识应用程序更容易编写，现有的知识网络更容易改进，全新的知识网络更容易构建。该小组的工作是基于他们开发的一种新颖而极其简洁的编程形式，这种编程形式允许同时编程和提取相关信息，以促进知识网络。拟议的同时编程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，通过提供额外的弱监督的信息提取管道，通常用于生产网络。该系统将在经济学领域的真实的数据和用户上进行测试。然而，这些方法和工具将不针对具体专题，而是应广泛适用于许多专题领域的知识网络。该奖项反映了NSF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。