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融合加速器支持以使用为灵感、以团队为基础的多学科努力，以应对国家重要性的挑战，并将在不久的将来产生对社会有价值的成果。该项目的目标是改进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“知识图谱”，这是一种专门支持具有地理空间组成部分的其他数据分析知识工具的知识图谱工具。GeoEnrichment描述了利用为地理空间研究领域量身定做的各种辅助信息(如人口统计数据)来扩充数据的过程。GeoEnrichment工具显著降低了获取、输入和清理地理数据所涉及的成本。遗憾的是，目前可用的地理浓缩服务仅提供对预定义类别的信息的访问，不能有效地处理相互关联的数据，对数据集成提供的支持有限，而且通常很昂贵。该项目计划使数据驱动的决策和数据分析大大提高效率、可及性和成本。该项目将把新的基于人工智能的地理富集化技术与知识图谱结合在一起，将跨越人-环境界面的开放、跨领域、密集集成的数据聚集在一起。这个项目的工作是由一个开放的、可自由使用的知识图谱实现的。这些图表是可伸缩的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派生的紧密互连的语句。这些技术工具旨在对需要地理空间情报的任何领域或跨领域活动的研究人员、分析人员、决策者和感兴趣的公众有用并可供其使用。该项目包括与非学术和学术利益攸关方的牢固伙伴关系，其中包括4个营利性组织、2个政府机构和1个非营利性组织，以及5个学术伙伴关系：ESRI(地理信息系统)、Oliver Wyman(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)、IN10T(数字农业、农场伙伴关系)、美国地质调查局(USGS)、美国农业部(USDA)内的自然资源保护局(USDA)：和直接救济(人道主义援助)；以及加州大学圣巴巴拉分校(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)。在这一第二阶段的努力中，预计将发展更多的伙伴关系。“知识图表”将成为汇聚加速器第二阶段项目的宝贵组成部分，为该项目中的其他项目提供地理空间工具。此外，该项目计划专注于几个可能造福美国社会的战略应用领域，包括：与新冠肺炎相关的供应链中断与美国食品、农业和能源部门及其一般供应链的关系；与农业可持续性、土壤保持实践和农场劳动力之间相互作用相关的环境政策问题；以及在美国国内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，这些语句由互连的节点和边标记的资源构成，允许相同节点的多条不同种类的边。指定知识图词汇表的含义的一组定义语句称为ITS(KG)模式或本体。本体论对于严格的逻辑解释和机器可操作性至关重要。知识图谱技术的几项创新将推动该项目：(I)创建一个开放的、网络可访问的知识图谱，并附带各种方法和工具，以便能够从各种来源对该图谱作出贡献；(Ii)制定将遥感图像和无人机图像等图像数据语义提升到该图表中的策略，从而整合大量数据；(Iii)开发新颖的空间显式人工智能方法、模型和服务，以便在该图表之上实现地理丰富；以及(Iv)为KnowWhere Graph开发可编程的(应用程序接口，API)和人类可访问的界面。通过将开放图形技术的灵活性、表现力和社区驱动的功能与多格式地理空间数据和高级地理空间智能相结合，KnowWhere Graph旨在成为一个丰富的综合信息资源，可以在众多领域和部门内和跨多个领域和部门转换和融合发现、分析和综合。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。

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

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

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