Know, Know Where, Knowwheregraph: A Densely Connected, Cross-Domain Knowledge Graph and Geo-Enrichment Service Stack for Applications in Environmental Intelligence

Authors

  • Krzysztof Janowicz University of California, Santa Barbara
  • Pascal Hitzler Kansas State University
  • Wenwen Li Arizona State University
  • Dean Rehberger Michigan State University
  • Mark Schildhauer National Center for Ecological Analysis and Synthesis
  • Rui Zhu University of California, Santa Barbara
  • Cogan Shimizu Kansas State University
  • Colby K. Fisher Hydronos Labs
  • Ling Cai University of California, Santa Barbara
  • Gengchen Mai Stanford University
  • Joseph Zalewski Kansas State University
  • Lu Zhou Kansas State University
  • Shirly Stephen University of California, Santa Barbara
  • Seila Gonzalez Michigan State University
  • Bryce Mecum National Center for Ecological Analysis and Synthesis
  • Anna Lopez Carr Direct Relief
  • Andrew Schroeder Direct Relief
  • Dave Smith University of California, Santa Barbara
  • Dawn Wright Esri
  • Sizhe Wang Arizona State University
  • Yuanyuan Tian Arizona State University
  • Zilong Liu University of California, Santa Barbara
  • Meilin Shi University of California, Santa Barbara
  • Anthony D’Onofrio Michigan State University
  • Zhining Gu Arizona State University
  • Kitty Currier University of California, Santa Barbara

DOI:

https://doi.org/10.1609/aimag.v43i1.19120

Abstract

Knowledge graphs (KGs) are a novel paradigm for the representation, retrieval, and integration of data from highly heterogeneous sources. Within just a few years, KGs and their supporting technologies have become a core component of modern search engines, intelligent personal assistants, business intelligence, and so on. Interestingly, despite large-scale data availability, they have yet to be as successful in the realm of environmental data and environmental intelligence. In this paper, we will explain why spatial data require special treatment, and how and when to semantically lift environmental data to a KG. We will present our KnowWhereGraph that contains a wide range of integrated datasets at the human–environment interface, introduce our application areas, and discuss geospatial enrichment services on top of our graph. Jointly, the graph and services will provide answers to questions such as “what is here,” “what happened here before,” and “how does this region compare to …” for any region on earth within seconds.

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Published

2022-03-31

How to Cite

Janowicz, K., Hitzler, P. ., Li, W. ., Rehberger, D. ., Schildhauer, M. ., Zhu, R. ., Shimizu, C. ., Fisher, C. ., Cai, L. ., Mai, G., Zalewski, J., Zhou, L., Stephen, S. ., Gonzalez, S. ., Mecum, B. ., Carr, A. ., Schroeder, A. ., Smith, D. ., Wright, D. ., Wang, S. ., Tian, Y. ., Liu, Z. ., Shi, M. ., D’Onofrio, A. ., Gu, Z. ., & Currier, K. . (2022). Know, Know Where, Knowwheregraph: A Densely Connected, Cross-Domain Knowledge Graph and Geo-Enrichment Service Stack for Applications in Environmental Intelligence. AI Magazine, 43(1), 30-39. https://doi.org/10.1609/aimag.v43i1.19120

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Special Topic Articles