Convergence Accelerator Phase I (RAISE): Credible Open Knowledge Network

融合加速器第一阶段（RAISE）：可信的开放知识网络

• 批准号: 1937143
• 负责人: Chengkai Li
• 金额: $ 99.99万
• 依托单位: University of Texas at Arlington
• 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 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1937143&HistoricalAwards=false
• 关键词: Convergence; Accelerator; Phase; RAISE; Credible

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 develop new capabilities to ensure the quality and credibility of information collected in large assemblages of data known as knowledge graphs or knowledge networks. Many of today's intelligent software products are powered by massive knowledge assemblages which are often proprietary. Developing an openly available infrastructure based on public data is one of the overarching tracks of the overall Convergence Accelerator Pilot in 2019. The goal of this specific project is to ensure credibility -- integrity, completeness and truthfulness -- in developing such an open knowledge network. As such, this project's efforts are likely to provide insights and value to many of the Convergence Accelerator Phase I efforts initiated in 2019. This project plans to develop a resource for debunking misinformation, which is important to decision making by individuals, organizations, communities, and policy makers. The project's initial use-cases will be healthcare, helping to create methodologies that ensure that health-related data assembled is reliable to support healthcare understanding and decision making, and mitigating security threats from software vulnerabilities by looking at the validity and reliability of indicators of compromise within collections of cyber threat intelligence. The project supports a multi-institutional and multidisciplinary team that has valuable expertise spanning computer science, economics, journalism and communication, political science, psychology, and public health. The team includes researchers from industrial partners, government research organizations, academic institutions, and international organizations with complementary expertise relevant to information credibility. Phase I of the project will focus on team formation, research planning, and developing proof-of-concept. If the project successfully proceeds to phase II it would develop a sustainable ecosystem of datasets, algorithms, software, as well as a stakeholder community for creating and fully utilizing credibility tools across an open knowledge network.The team will conduct use-inspired, convergence research on several cross-cutting problems: (1) Modeling credible knowledge graphs, deciding what types of knowledge need to be captured in order to promote credibility, and how such knowledge should be represented in order to enable computational approaches. (2) Developing data-driven understanding of what factors contribute to the persuasiveness of factual statements, what signals help gauge credibility, and how to employ that information to envision countermeasures against misinformation. (3) Designing computational methods that leverage knowledge graphs to vet statements and generate verifiable and reliable explanations. (4) Procedures and mechanisms for initiating, developing, and maintaining a credible open knowledge network over time. The findings of this research have the potential to significantly impact understanding of information and mis-information creation and consumption and may trigger new lines of investigation while also helping to create more reliable information in knowledge networks generally.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.

美国国家科学基金会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，并显示在不久的将来交付的潜力。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开发新的能力，以确保在称为知识图或知识网络的大型数据集合中收集的信息的质量和可信度。今天的许多智能软件产品都是由大量的知识组合提供动力的，这些知识组合通常是专有的。开发基于公共数据的开放式基础设施是2019年整体融合加速器试点的首要目标之一。这一具体项目的目标是确保建立这样一个开放知识网络的可信性，即完整性、完整性和真实性。因此，该项目的努力可能会为2019年启动的许多融合加速器第一阶段工作提供见解和价值。该项目计划开发一种用于揭穿错误信息的资源，这对个人，组织，社区和政策制定者的决策非常重要。该项目的初始用例将是医疗保健，帮助创建方法，确保收集的健康相关数据可靠，以支持医疗保健理解和决策，并通过查看网络威胁情报集合中妥协指标的有效性和可靠性来减轻软件漏洞的安全威胁。该项目支持一个多机构和多学科的团队，拥有宝贵的专业知识，涵盖计算机科学，经济学，新闻和传播，政治学，心理学和公共卫生。该团队包括来自工业合作伙伴、政府研究机构、学术机构和国际组织的研究人员，他们具有与信息可信度相关的互补专业知识。该项目的第一阶段将侧重于团队组建、研究规划和开发概念验证。如果该项目成功进入第二阶段，它将开发一个可持续的数据集、算法、软件生态系统，以及一个利益相关者社区，以创建和充分利用开放知识网络中的可信度工具。该团队将对几个跨领域问题进行基于使用的融合研究：（1）建立可信的知识图谱模型，决定需要获取哪些类型的知识以提高可信度，以及如何表示这些知识以实现计算方法。(2)发展数据驱动的理解，了解哪些因素有助于事实陈述的说服力，哪些信号有助于衡量可信度，以及如何利用这些信息来设想针对错误信息的对策。(3)设计计算方法，利用知识图来审查陈述并生成可验证和可靠的解释。(4)随着时间的推移，启动、发展和维护可信的开放知识网络的程序和机制。这项研究的结果有可能对信息和错误信息的创造和消费产生重大影响，并可能引发新的调查路线，同时也有助于在知识网络中创造更可靠的信息。该奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

A Dashboard for Mitigating the COVID-19 Misinfodemic

• DOI: 10.18653/v1/2021.eacl-demos.12
• 发表时间: 2021
• 作者: Zhengyuan Zhu;Kevin Meng;Josue Caraballo;Israa Jaradat;Xiao Shi;Zeyu Zhang;F. Akrami;Haojin Liao;Fatma Arslan;Damian Jimenez;Mohanmmed Samiul Saeef;P. Pathak;Chengkai Li

GRIP: Constraint-based Explanation of Missing Answers for Graph Queries

• DOI: 10.1145/3448016.3452758
• 发表时间: 2021-06
• 期刊: Proceedings of the 2021 International Conference on Management of Data
• 作者: Qi Song;Hanchao Ma;Peng Lin;Yinghui Wu

Spatiotemporal Graph Neural Network for Performance Prediction of Photovoltaic Power Systems

• DOI: 10.1609/aaai.v35i17.17799
• 发表时间: 2021-05
• 作者: A. M. Karimi;Yinghui Wu;Mehmet Koyutürk;R. French

Kronos: Lightweight Knowledge-based Event Analysis in Cyber-Physical Data Streams

Kronos：网络物理数据流中基于知识的轻量级事件分析

• DOI: 10.1109/icde48307.2020.00165
• 发表时间: 2020
• 期刊: 2020 IEEE 36th International Conference on Data Engineering (ICDE
• 作者: Namaki, Mohammad Hossein;Zhang, Xin;Singh, Sukhjinder;Ahmed, Arman;Foroutan, Armina;Wu, Yinghui;Srivastava, Anurag K.;Kocheturov, Anton
• 通讯作者: Kocheturov, Anton

GEDet: detecting erroneous nodes with a few examples

GEDet：用几个例子检测错误节点

• DOI: 10.14778/3476311.3476367
• 发表时间: 2021
• 期刊: Proceedings of the VLDB Endowment
• 影响因子: 2.5
• 作者: Guan, Sheng;Ma, Hanchao;Choudhury, Sutanay;Wu, Yinghui
• 通讯作者: Wu, Yinghui