Convergence Accelerator Phase I (RAISE): Spatially-Explicit Models, Methods, and Services for Open Knowledge Networks
融合加速器第一阶段 (RAISE):开放知识网络的空间显式模型、方法和服务
基本信息
- 批准号:1936677
- 负责人:
- 金额:$ 99.95万
- 依托单位:
- 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:
- 关键词:
项目摘要
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 be to help connect the vast community of geographic information systems users in industry, government agencies, academia, and the broader public, to knowledge graphs. Knowledge graphs consist of interlinked pieces of information about our world. By connecting data across different domains, multimedia formats, and perspectives, knowledge graphs enable users to ask more complicated questions and arrive at a more holistic understanding of complex physical and social processes. This project focuses on the tools needed to identify and link space and time for knowledge graphs. These tools are important because everything happens somewhere and at some time and because knowing where and when things happen is critical to understanding why and how they happen. Currently geographic information systems in use in academia, industry, and governments are not yet well integrated with knowledge graphs. This project's highly interdisciplinary team (representing four major universities, three industry partners, and two government agencies) will demonstrate how to develop common models, methods, and services to enable the publication, retrieval, reuse, analysis, and inference of spatial and temporal data for knowledge graphs across domain boundaries. The team plans to will apply the techniques developed to applications such as soil health, hydrology, and urban planning. Expected project results will give domain experts and the broader public free and open access to data and analysis functions that will link otherwise disconnected knowledge about extreme events, soil health, smart farming, and urban planning together. The open knowledge network envisioned in this effort has the potential to provide easier access to knowledge graph data which may improve opportunities for many sectors that rely on connecting to geographic information system data. Places connect people, entities, and events together and, thus, are a densely interconnected part of all general-purpose knowledge graphs to date. Nonetheless, individual domains have developed their own (often incompatible) ways to represent places as well as space and time more generally. For instance, there is no agreed upon method to spatio-temporally restrict statements such as changing national boundaries. Similarly, established machine-learning techniques used to infer new knowledge graph statements or to summarize existing ones typically ignore spatial and temporal aspects and, thus, fall short of their full potential. Finally, existing efforts cannot handle numerical or multi-modal data such as satellite imagery. The overarching theme of this Phase I project is to reach convergence on how to efficiently represent, retrieve, and analyze the spatial and temporal aspects present in almost all datasets irrespective of the individual disciplines they originate from, resulting in interoperability instead of domain-specific and potentially incompatible solutions. During phase I the team will review state-of-the-art methods, models, and services; harmonize them across domain boundaries; develop spatially and temporally explicit machine-learning methods and tools to better represent, analyze, and infer spatial and temporal data; and provide best practices for other open knowledge networks.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有助于将工业、政府机构、学术界和更广泛的公众中的广大地理信息系统用户社区与知识图谱联系起来。知识图谱由关于我们世界的相互关联的信息组成。通过连接不同领域、多媒体格式和视角的数据,知识图谱使用户能够提出更复杂的问题,并对复杂的物理和社会过程有更全面的理解。该项目的重点是识别和链接知识图的空间和时间所需的工具。这些工具很重要,因为每件事都发生在某个地方和某个时间,因为知道事情发生的地点和时间对于理解它们为什么和如何发生至关重要。目前,学术界、工业界和政府使用的地理信息系统尚未与知识图谱很好地集成。该项目的高度跨学科团队(代表四所主要大学,三个行业合作伙伴和两个政府机构)将展示如何开发通用模型,方法和服务,以实现跨领域边界的知识图的空间和时间数据的发布,检索,重用,分析和推理。该团队计划将开发的技术应用于土壤健康,水文和城市规划等应用。预期的项目成果将使领域专家和更广泛的公众能够免费开放地访问数据和分析功能,这些功能将把极端事件、土壤健康、智能农业和城市规划等方面的知识联系起来。在这项工作中设想的开放式知识网络有可能使人们更容易获得知识图数据,从而为依赖与地理信息系统数据连接的许多部门提供更多机会。地点将人、实体和事件连接在一起,因此是迄今为止所有通用知识图中紧密互连的一部分。尽管如此,各个域已经发展出了自己的(通常不兼容的)方式来更普遍地表示空间和时间。例如,没有商定的方法来限制时空声明,例如更改国界。类似地,用于推断新知识图语句或总结现有知识图语句的已建立的机器学习技术通常忽略空间和时间方面,因此未能充分发挥其潜力。最后,现有的努力无法处理卫星图像等数字或多模式数据。这个第一阶段项目的首要主题是如何有效地表示,检索和分析几乎所有数据集中存在的空间和时间方面,而不管它们来自哪个学科,从而实现互操作性,而不是特定领域和潜在的不兼容解决方案。在第一阶段,该团队将审查最先进的方法,模型和服务;跨领域边界协调它们;开发空间和时间明确的机器学习方法和工具,以更好地表示,分析和推断空间和时间数据;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学术价值和更广泛的影响审查标准。
项目成果
期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Completion Reasoning Emulation for the Description Logic EL+
描述逻辑 EL 的完成推理仿真
- DOI:
- 发表时间:2020
- 期刊:
- 影响因子:0
- 作者:Eberhart, A.;Ebrahimi, M.;Zhou, L.;Shimizu, C.;Hitzler, P.
- 通讯作者:Hitzler, P.
Semantically-Enriched Search Engine for Geoportals: A Case Study with ArcGIS Online
地理门户的语义丰富搜索引擎:ArcGIS Online 案例研究
- DOI:10.5194/agile-giss-1-13-2020
- 发表时间:2020
- 期刊:
- 影响因子:0
- 作者:Mai, Gengchen;Janowicz, Krzysztof;Prasad, Sathya;Shi, Meilin;Cai, Ling;Zhu, Rui;Regalia, Blake;Lao, Ni
- 通讯作者:Lao, Ni
GeoLink Cruises: A Non-Synthetic Benchmark for Co-Reference Resolution on Knowledge Graphs
- DOI:10.1145/3340531.3412770
- 发表时间:2020-10
- 期刊:
- 影响因子:0
- 作者:Reihaneh Amini;Lu Zhou;P. Hitzler
- 通讯作者:Reihaneh Amini;Lu Zhou;P. Hitzler
Towards evaluating complex ontology alignments
评估复杂的本体对齐
- DOI:10.1017/s0269888920000168
- 发表时间:2020
- 期刊:
- 影响因子:0
- 作者:Zhou, Lu;Thiéblin, Elodie;Cheatham, Michelle;Faria, Daniel;Pesquita, Catia;Trojahn, Cassia;Zamazal, Ondřej
- 通讯作者:Zamazal, Ondřej
When Spatial Analytics Meets Cyberinfrastructure: an Interoperable and Replicable Platform for Online Spatial-Statistical-Visual Analytics
- DOI:10.1007/s41651-020-00056-5
- 发表时间:2020-06
- 期刊:
- 影响因子:4
- 作者:H. Shao;Wenwen Li;Wei Kang;Sergio J. Rey
- 通讯作者:H. Shao;Wenwen Li;Wei Kang;Sergio J. Rey
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Krzysztof Janowicz其他文献
Evidence for existence of molecular stemness markers in porcine ovarian follicular granulosa cells
猪卵巢滤泡颗粒细胞中存在分子干性标记的证据
- DOI:
- 发表时间:
2019 - 期刊:
- 影响因子:0
- 作者:
K. Stefańska;Rafał Sibiak;Greg Hutchings;C. Dompe;Lisa Moncrieff;Krzysztof Janowicz;M. Ješeta;B. Kempisty;M. Machatkova;P. Mozdziak - 通讯作者:
P. Mozdziak
Diverse data! Diverse schemata?
数据多样!
- DOI:
10.3233/sw-210453 - 发表时间:
2021 - 期刊:
- 影响因子:3
- 作者:
Krzysztof Janowicz;Cogan Shimizu;Pascal Hitzler;Gengchen Mai;Shirly Stephen;Rui Zhu;Ling Cai;Lu Zhou;Mark Schildhauer;Zilong Liu;Zhan Wang;Meilin Shi - 通讯作者:
Meilin Shi
A Pattern for Modeling Causal Relations Between Events
事件之间因果关系建模的模式
- DOI:
- 发表时间:
2021 - 期刊:
- 影响因子:0
- 作者:
C. Shimizu;Rui Zhu;M. Schildhauer;Krzysztof Janowicz;P. Hitzler - 通讯作者:
P. Hitzler
The Sigspatial Special Newsletter of the Association for Computing Machinery Special Interest Group on Spatial Information the Sigspatial Special Table of Contents Section 1: Special Issue on Mobile Data Analytics Introduction to This Special Issue: Mobile Data Analytics…………...………...………. Chi-yin Cho
计算机协会空间信息特别兴趣小组的 Sigspatial 特刊通讯 Sigspatial 特刊目录 第 1 部分:移动数据分析特刊 本特刊简介:移动数据分析…………………… .. .…….
- DOI:
- 发表时间:
- 期刊:
- 影响因子:0
- 作者:
Chair;M. Mokbel;Chi;Yanbing Shen;Ying Huang;Zhao;Jie Bao;Defu Lian;Fuzheng Zhang;Nicholas Jing Yuan;Ting Hua;Liang Zhao;Feng Chen;Chang;Grant McKenzie;Krzysztof Janowicz;Gueorgi Kossinets;Hui Zhang;Yan Huang;J. Thill;Ying Zhang;B. Priyantha;Chengyang Zhang;F. Banaei;Abdeltawab M. Hendawi;Acm Sigspatial;Hong Kong;Bilong Shen;Yingman Zhao - 通讯作者:
Yingman Zhao
Overview of methods of isolation, cultivation and genetic profiling on human umbilical cord stem cells
人脐带干细胞分离、培养和基因分析方法概述
- DOI:
10.2478/acb-2019-0023 - 发表时间:
2019 - 期刊:
- 影响因子:0
- 作者:
K. Stefańska;Rafał Sibiak;C. Dompe;Lisa Moncrieff;Greg Hutchings;Krzysztof Janowicz;B. Kempisty - 通讯作者:
B. Kempisty
Krzysztof Janowicz的其他文献
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{{ truncateString('Krzysztof Janowicz', 18)}}的其他基金
A1: KnowWhereGraph: Enriching and Linking Cross-Domain Knowledge Graphs using Spatially-Explicit AI Technologies
A1:KnowWhereGraph:使用空间显式人工智能技术丰富和链接跨领域知识图
- 批准号:
2033521 - 财政年份:2020
- 资助金额:
$ 99.95万 - 项目类别:
Cooperative Agreement
RAPID: COVIDGeoGraph – A Geographically Integrated Cross-Domain Knowledge Graph for Studying Regional Disruptions
RAPID:COVIDGeoGraph — 用于研究区域中断的地理集成跨域知识图
- 批准号:
2028310 - 财政年份:2020
- 资助金额:
$ 99.95万 - 项目类别:
Standard Grant
EarthCube IA: Collaborative Proposal: Cross-Domain Observational Metadata Environmental Sensing Network (X-DOMES)
EarthCube IA:协作提案:跨域观测元数据环境传感网络(X-DOMES)
- 批准号:
1540849 - 财政年份:2015
- 资助金额:
$ 99.95万 - 项目类别:
Standard Grant
III: Travel Fellowships for Students from U.S. Universities to Attend ISWC 2013
III:为美国大学学生提供参加 ISWC 2013 的旅行奖学金
- 批准号:
1345449 - 财政年份:2013
- 资助金额:
$ 99.95万 - 项目类别:
Standard Grant
Student Travel Fellowships: 2013 Web Reasoning and Rule Systems Conference
学生旅行奖学金:2013 年网络推理和规则系统会议
- 批准号:
1344437 - 财政年份:2013
- 资助金额:
$ 99.95万 - 项目类别:
Standard Grant
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大规模非确定图数据分析及其Multi-Accelerator并行系统架构研究
- 批准号:62002350
- 批准年份:2020
- 资助金额:24.0 万元
- 项目类别:青年科学基金项目
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