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

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arxiv 2502.13874 v2 pith:XNKCAVMP submitted 2025-02-19 cs.DB

classification cs.DB
keywords dataknowledgeknowwheregraphgeospatialintegrationaddressdatasetsgraph
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Whose Truth? Pluralistic Geo-Alignment for (Agentic) AI

    cs.AI 2025-08 conditional novelty 6.0 of 10

    Geo-alignment means matching an AI system's output distribution to the locally appropriate distribution for each query, location, and time, and the paper argues spatial structure makes that target learnable.

  2. Knowledge Conceptualization Impacts RAG Efficacy

    cs.AI 2025-07 conditional novelty 6.0 of 10

    An empirical study showing that both schema complexity and representation format affect how well GPT-4o generates SPARQL queries from competency questions, with mixed results across two knowledge graph families.

  3. FlyMeThrough: Human-AI Collaborative 3D Indoor Mapping with Commodity Drones

    cs.HC 2025-08 conditional novelty 5.0 of 10

    A commodity-drone, RGB-only pipeline with human-AI annotation produces 3D indoor maps with localized points of interest, evaluated in 11 of 12 scanned buildings.

  4. Querying Climate Knowledge: Semantic Retrieval for Scientific Discovery

    cs.CL 2025-09 reject novelty 2.0 of 10

    A climate knowledge graph built from prior extraction work is presented with example queries, but without evaluation or released artifacts.

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