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The KnowWhereGraph Ontology

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arxiv 2410.13948 v1 pith:YSXJ6WXF submitted 2024-10-17 cs.AI

Cogan Shimizu , Shirly Stephe , Adrita Barua , Ling Cai , Antrea Christou , Kitty Currier , Abhilekha Dalal , Colby K. Fisher
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Pascal Hitzler Krzysztof Janowicz Wenwen Li Zilong Liu Mohammad Saeid Mahdavinejad Gengchen Mai Dean Rehberger Mark Schildhauer Meilin Shi Sanaz Saki Norouzi Yuanyuan Tian Sizhe Wang Zhangyu Wang Joseph Zalewski Lu Zhou Rui Zhu
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classification cs.AI
keywords knowwheregraphontologygraphschemasoilactsaddressagricultural
verification ladder T0 review T1 audit T2 compute T3 formal
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KnowWhereGraph is one of the largest fully publicly available geospatial knowledge graphs. It includes data from 30 layers on natural hazards (e.g., hurricanes, wildfires), climate variables (e.g., air temperature, precipitation), soil properties, crop and land-cover types, demographics, and human health, various place and region identifiers, among other themes. These have been leveraged through the graph by a variety of applications to address challenges in food security and agricultural supply chains; sustainability related to soil conservation practices and farm labor; and delivery of emergency humanitarian aid following a disaster. In this paper, we introduce the ontology that acts as the schema for KnowWhereGraph. This broad overview provides insight into the requirements and design specifications for the graph and its schema, including the development methodology (modular ontology modeling) and the resources utilized to implement, materialize, and deploy KnowWhereGraph with its end-user interfaces and public query SPARQL endpoint.

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  1. Curate, Connect, Inquire: A System for Findable Accessible Interoperable and Reusable (FAIR) Human-Robot Centered Datasets

    cs.IR 2025-05 conditional novelty 5.0 of 10

    A curation pipeline with a shared data model, a repository, a knowledge graph, and a ChatGPT-based chatbot that lets researchers ask questions across human-robot datasets.

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