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

Krzysztof Janowicz, Pascal Hitzler, Wenwen Li, Dean Rehberger, Mark Schildhauer, Rui Zhu, Cogan Shimizu, Colby K. Fisher, Ling Cai, Gengchen Mai, Joseph Zalewski, Lu Zhou, Shirly Stephen, Seila Gonzalez, Bryce Mecum, Anna Lopez-Carr, Andrew Schroeder, David Smith, Dawn Wright, Sizhe WangYuanyuan Tian, Zilong Liu, Meilin Shi, Anthony D’onofrio, Zhining Gu, Kitty Currier

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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 happenedherebefore,”and“howdoesthisregioncompareto…”foranyregionon earth within seconds.

Original languageEnglish (US)
Pages (from-to)30-39
Number of pages10
JournalAI Magazine
Volume43
Issue number1
DOIs
StatePublished - Mar 1 2022
Externally publishedYes

ASJC Scopus subject areas

  • Artificial Intelligence

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