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The KnowWhereGraph: A Large-Scale Geo-Knowledge Graph for Interdisciplinary Knowledge Discovery and Geo-Enrichment

  • Rui Zhu
  • , Cogan Shimizu
  • , Shirly Stephen
  • , Colby K. Fisher
  • , Thomas Thelen
  • , Kitty Currier
  • , Krzysztof Janowicz
  • , Pascal Hitzler
  • , Mark Schildhauer
  • , Wenwen Li
  • , Dean Rehberger
  • , Adrita Barua
  • , Antrea Christou
  • , Ling Cai
  • , Abhilekha Dalal
  • , Anthony D'Onofrio
  • , Andrew Eells
  • , Mitchell Faulk
  • , Zilong Liu
  • , Gengchen Mai
  • Mohammad Saeid Mahdavinejad, Bryce Mecum, Sanaz Saki Norouzi, Meilin Shi, Yuanyuan Tian, Sizhe Wang, Zhangyu Wang, Joseph Zalewski

Research output: Contribution to journalArticlepeer-review

Abstract

Global challenges such as food supply chain disruptions, public health crises, and natural hazard responses require access to and integration of diverse datasets, many of which are geospatial. Over the past few years, a growing number of (geo)portals have been developed to address this need. However, most existing (geo)portals are stacked by separated or sparsely connected data “silos” impeding effective data consolidation. A new way of sharing and reusing geospatial data is therefore urgently needed. In this work, we introduce KnowWhereGraph, a knowledge graph-based data integration, enrichment, and synthesis framework that not only includes schemas and data related to human and environmental systems but also provides a suite of supporting tools for accessing this information. The KnowWhereGraph aims to address the challenge of data integration by building a large-scale, cross-domain, preintegrated, FAIR-principles-based, and AI-ready data warehouse rooted in knowledge graphs. We highlight the design principles of KnowWhereGraph, emphasizing the roles of space, place, and time in bridging various data “silos.” Additionally, we demonstrate multiple use cases where the proposed geospatial knowledge graph and its associated tools empower decision-makers to uncover insights that are often hidden within complex and poorly interoperable datasets.
Original languageEnglish
Article numbere70184
JournalTransactions in GIS
Volume30
Issue number1
DOIs
StatePublished - Feb 2026

ASJC Scopus Subject Areas

  • General Earth and Planetary Sciences

Keywords

  • GeoAI
  • geoenrichment
  • geospatial knowledge graphs
  • geospatial semantics
  • knowledge discovery
  • spatial data infrastructure

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