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

Where it appeared

Published inAI Magazine
PublisherWiley
Volume43
Issue1
Pages30--39

Identifiers

DOI10.1609/aimag.v43i1.19120
ISSN2371-9621

From crossref.

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.

How it got here

How it got hereagent via crossref
Added2026-10-23 13:51 UTC
Not denied bya person 2026-10-29 17:12 UTC

Cite it as

@article{janowicz2022know,
  title        = {Know, Know Where, Knowwheregraph: A Densely Connected, Cross-Domain Knowledge Graph and Geo-Enrichment Service Stack for Applications in Environmental Intelligence},
  author       = {Krzysztof Janowicz and Pascal Hitzler and Wenwen Li and Dean Rehberger and Mark Schildhauer and Rui Zhu and Cogan Shimizu and Colby Fisher and Ling Cai and Gengchen Mai and Joseph Zalewski and Lu Zhou and Shirly Stephen and Seila Gonzalez and Bryce Mecum and Anna Carr and Andrew Schroeder and Dave Smith and Dawn Wright and Sizhe Wang and Yuanyuan Tian and Zilong Liu and Meilin Shi and Anthony D’Onofrio and Zhining Gu and Kitty Currier},
  year         = {2022},
  journal      = {AI Magazine},
  publisher    = {Wiley},
  volume       = {43},
  number       = {1},
  pages        = {30--39},
  issn         = {2371-9621},
  doi          = {10.1609/aimag.v43i1.19120},
}

This record lives at https://refs.drheap.org/janowicz2022know/ and will keep doing so.