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
• 负责人: Krzysztof Janowicz
• 金额: $ 499.89万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2033521&HistoricalAwards=false
• 关键词: A1; KnowWhereGraph; Enriching; Linking; Cross

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future.The goal of this project is to improve data-driven decision making and data analytics, specifically data analytics that involve geographic data. This project will create the “KnowWhereGraph” – a knowledge graph tool that specifically enables other data-analysis knowledge tools that have a geospatial component. GeoEnrichment describes the process by which data becomes augmented with a wide range of auxiliary information tailored to a geospatial study area (such as demographic data). GeoEnrichment tools significantly reduce the costs involved in acquiring, entering, and cleaning geo-data. Unfortunately, currently available geoenrichment services provide access to only pre-defined categories of information, do not effectively handle interconnected data, offer limited support for data integration, and are generally expensive. This project plans to make data-driven decision making and data analytics substantially more effective, accessible, and affordable. The project will merge novel Artificial Intelligence-based geoenrichment technologies with a knowledge graph that brings together open, cross-domain, densely integrated data spanning the human-environment interface. This project’s work is enabled by an open, freely usable knowledge graph. These graphs are a combination of scalable, Web-standard technologies, specifications, and data cultures for representing densely interconnected statements derived from structured or unstructured data across domains, in both human and machine-readable ways. The technology tools are designed to be useful to and useable by researchers, analysts, decision-makers, and the interested public in any domain or cross-domain activity requiring geospatial intelligence. This project includes strong partnerships with non-academic and academic stakeholders including 4 for-profit organizations, 2 government agencies, and one non-profit, as well as five academic partnerships: ESRI (Geographic Information Systems); Oliver Wyman, (commodity markets and supply chains), Princeton Climate Analytics (weather and climate information services), In10T (digital agriculture, farm partnerships); US Geological Survey (USGS), Natural Resources Conservation Service within the U.S. Department of Agriculture (USDA): and DirectRelief (humanitarian aid); as well as University of California Santa Barbara(UCSB), Kansas State University (K-State), Michigan State University (MSU), Arizona State University (ASU), and University of Southern California(USC). Additional partnerships are expected to develop during this Phase II effort. The “KnowWhereGraph” will be a valuable element of the Convergence Accelerator Phase II cohort, providing geospatial tools to the other projects within the cohort. In addition the project plans to focus on several strategic application areas that are likely to benefit US society, including: COVID-19 related supply chain disruptions and the US food, agriculture, and energy sectors, and their attendant supply chains generally; environmental policy issues relative to interactions among agricultural sustainability, soil conservation practice, and farm labor; and delivery of emergency humanitarian aid, within the US and internationally. Anytime knowing “where” is key, this project’s tools may be helpful. Formally, a knowledge graph consists of a massive set of statements, constructed from inter-connected node- and edge-labeled resources, allowing multiple, heterogeneous edges for the same nodes. A collection of definitional statements specifying the meaning of the knowledge graph's vocabulary is called its (KG) schema or ontology. The ontology is critical for rigorous logical interpretation and machine-actionability. Several innovations in knowledge graph technology will drive the project: (I) creating an open, web-accessible knowledge graph, with attendant methods and tools, to enable contributions to the graph from a range of sources; (II) developing strategies for semantically lifting imagery data, such as remotely sensed imagery and drone imagery, into this graph, thereby integrating vast amounts of data; (III) developing novel spatially-explicit AI-based methods, models, and services to enable geoenrichment on top of this graph; and (IV) developing both programmatic (application program interface, API) and human-accessible interfaces for the KnowWhereGraph. By merging the flexibility, expressive power, and community-driven features of open graph technologies with multi-format geospatial data and advanced geospatial intelligence, the KnowWhereGraph is designed to become a rich, integrative information resource that can transform and converge discovery, analysis, and synthesis within and across a multitude of fields and sectors.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 Convergence Accelerator支持以使用为灵感的、基于团队的、多学科的努力，以应对国家重要性的挑战，并在不久的将来为社会提供有价值的成果。该项目的目标是改善数据驱动的决策和数据分析，特别是涉及地理数据的数据分析。该项目将创建“KnowWhereGraph”-一个专门支持具有地理空间组件的其他数据分析知识工具的知识图工具。GeoEnrichment描述了一个过程，通过该过程，数据通过针对地理空间研究领域（如人口统计数据）量身定制的各种辅助信息得到增强。GeoEnrichment工具显著降低了获取、输入和清理地理数据的成本。不幸的是，目前可用的地理富集服务仅提供对预定义类别的信息的访问，不能有效地处理互连数据，对数据集成提供有限的支持，并且通常是昂贵的。该项目计划使数据驱动的决策和数据分析更加有效，可访问和负担得起。该项目将把新的基于人工智能的地理富集技术与知识图谱相结合，汇集了跨越人类环境界面的开放、跨领域、密集集成的数据。这个项目的工作是由一个开放的，可免费使用的知识图谱。这些图是可扩展的Web标准技术、规范和数据文化的组合，用于以人类和机器可读的方式表示从跨域的结构化或非结构化数据中派生的密集互连的语句。这些技术工具旨在为研究人员、分析人员、决策者和感兴趣的公众在需要地理空间情报的任何领域或跨领域活动中提供有用和可用的信息。该项目包括与非学术和学术利益相关者的强大合作伙伴关系，包括4个营利组织，2个政府机构和1个非营利组织，以及5个学术合作伙伴关系：ESRI（地理信息系统）;奥利弗怀曼，（商品市场和供应链），普林斯顿气候分析（天气和气候信息服务），In 10 T（数字农业，农场合作伙伴关系）;美国地质调查局（USGS），美国农业部（USDA）自然资源保护局：和DirectRelief（人道主义援助）;以及加州圣巴巴拉大学（UCSB）、堪萨斯州立大学（K-State）、密歇根州立大学（MSU）、亚利桑那州立大学（ASU）和南加州大学（USC）。预计在第二阶段工作期间将发展更多的伙伴关系。“KnowWhereGraph”将成为融合加速器第二阶段的一个重要组成部分，为该阶段的其他项目提供地理空间工具。此外，该项目计划重点关注可能使美国社会受益的几个战略应用领域，包括：与COVID-19相关的供应链中断和美国食品，农业和能源部门及其伴随的供应链;与农业可持续性，土壤保护实践和农场劳动力之间相互作用相关的环境政策问题;在美国和国际上提供紧急人道主义援助。任何时候知道“在哪里”是关键，这个项目的工具可能会有所帮助。从形式上讲，知识图由大量语句组成，这些语句由相互连接的节点和边标记的资源构建，允许相同节点有多个异构边。指定知识图的词汇表的含义的定义语句的集合被称为其（KG）模式或本体。本体对于严格的逻辑解释和机器可操作性至关重要。知识图谱技术方面的几项创新将推动该项目：㈠创建一个开放的、可在网上访问的知识图谱，并配备相应的方法和工具，以便能够从一系列来源对图谱作出贡献; ㈡制定战略，从语义上将遥感图像和无人机图像等图像数据提升到该图谱中，从而整合大量数据;（III）开发新的基于空间显式AI的方法、模型和服务，以在该图之上实现地理富集;以及（IV）为KnowWhereGraph开发编程（应用程序接口，API）和人类可访问的接口。通过将开放图技术的灵活性、表达能力和社区驱动功能与多格式地理空间数据和高级地理空间智能相结合，KnowWhereGraph旨在成为一种丰富的集成信息资源，可以转换和融合发现、分析、该奖项反映了NSF的法定使命，并被认为值得支持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。

项目成果

Know, Know Where, KnowWhereGraph: A densely connected, cross‐domain knowledge graph and geo‐enrichment service stack for applications in environmental intelligence

Know、KnowWhere、KnowWhereGraph：用于环境情报应用的密集连接的跨领域知识图谱和地理丰富服务堆栈

• DOI: 10.1002/aaai.12043
• 发表时间: 2022
• 期刊: AI Magazine
• 影响因子: 0.9
• 作者: Janowicz, Krzysztof;Hitzler, Pascal;Li, Wenwen;Rehberger, Dean;Schildhauer, Mark;Zhu, Rui;Shimizu, Cogan;Fisher, Colby K.;Cai, Ling;Mai, Gengchen
• 通讯作者: Mai, Gengchen

Modular ontology modeling

• DOI: 10.3233/sw-222886
• 发表时间: 2023-01-01
• 期刊: SEMANTIC WEB
• 影响因子: 3
• 作者: Shimizu，Cogan;Hammar，Karl;Hitzler，Pascal
• 通讯作者: Hitzler，Pascal

Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions

地理问答：挑战、独特性、分类和未来方向

• 发表时间: 2021
• 期刊: ArXivorg
• 作者: Gengchen Mai, Krzysztof Janowicz

Towards bridging the neuro-symbolic gap: deep deductive reasoners

• DOI: 10.1007/s10489-020-02165-6
• 发表时间: 2021-02
• 期刊: Applied Intelligence
• 影响因子: 5.3
• 作者: Monireh Ebrahimi;Aaron Eberhart;Federico Bianchi;P. Hitzler

Performance benchmark on semantic web repositories for spatially explicit knowledge graph applications

• DOI: 10.1016/j.compenvurbsys.2022.101884
• 发表时间: 2022-12
• 期刊: Comput. Environ. Urban Syst.
• 作者: Wenwen Li;Sizhe Wang;Sheng Wu;Zhining Gu;Yuanyuan Tian