The KnowWhereGraph: A Large-Scale Geo-Knowledge Graph for Interdisciplinary Knowledge Discovery and Geo-Enrichment
🤖 AI Summary
Geographic data portals commonly suffer from data silos, hindering cross-domain integration and knowledge discovery. To address this, we introduce KnowWhereGraph—the first large-scale, cross-domain geographic knowledge graph—built upon a novel three-layer semantic architecture anchored in space, place, and time, enabling unified representation of heterogeneous human–environment system data. Guided by FAIR principles and grounded in spatiotemporal ontology modeling, we develop a pre-integrated, AI-ready, full-stack geographic data fusion framework. We further design a toolchain comprising semantic mapping, entity linking, and GeoAI interfaces to support scalable knowledge graph construction and querying. Evaluated across food supply chains, public health, and disaster response, KnowWhereGraph significantly enhances data discoverability and latent relational inference, demonstrating robust cross-domain interoperability and actionable decision-support capabilities.
Application Category
📝 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, pre-integrated, 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.
Problem
Research questions and friction points this paper is trying to address.
Integrating diverse geospatial data sources
Overcoming data silo fragmentation
Enabling interdisciplinary knowledge discovery
Innovation
Methods, ideas, or system contributions that make the work stand out.
Knowledge graph integration
FAIR principles application
Geospatial data enrichment
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