Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.13874 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917930603642880 |
|---|---|
| author | Zhu, Rui Shimizu, Cogan Stephen, Shirly Fisher, Colby K. Thelen, Thomas Currier, Kitty Janowicz, Krzysztof Hitzler, Pascal Schildhauer, Mark Li, Wenwen Rehberger, Dean Barua, Adrita Christou, Antrea Cai, Ling Dalal, Abhilekha D'Onofrio, Anthony Eells, Andrew Faulk, Mitchell Liu, Zilong Mai, Gengchen Mahdavinejad, Mohammad Saeid Mecum, Bryce Norouzi, Sanaz Saki Shi, Meilin Tian, Yuanyuan Wang, Sizhe Wang, Zhangyu Zalewski, Joseph |
| author_facet | Zhu, Rui Shimizu, Cogan Stephen, Shirly Fisher, Colby K. Thelen, Thomas Currier, Kitty Janowicz, Krzysztof Hitzler, Pascal Schildhauer, Mark Li, Wenwen Rehberger, Dean Barua, Adrita Christou, Antrea Cai, Ling Dalal, Abhilekha D'Onofrio, Anthony Eells, Andrew Faulk, Mitchell Liu, Zilong Mai, Gengchen Mahdavinejad, Mohammad Saeid Mecum, Bryce Norouzi, Sanaz Saki Shi, Meilin Tian, Yuanyuan Wang, Sizhe Wang, Zhangyu Zalewski, Joseph |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_13874 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The KnowWhereGraph: A Large-Scale Geo-Knowledge Graph for Interdisciplinary Knowledge Discovery and Geo-Enrichment Zhu, Rui Shimizu, Cogan Stephen, Shirly Fisher, Colby K. Thelen, Thomas Currier, Kitty Janowicz, Krzysztof Hitzler, Pascal Schildhauer, Mark Li, Wenwen Rehberger, Dean Barua, Adrita Christou, Antrea Cai, Ling Dalal, Abhilekha D'Onofrio, Anthony Eells, Andrew Faulk, Mitchell Liu, Zilong Mai, Gengchen Mahdavinejad, Mohammad Saeid Mecum, Bryce Norouzi, Sanaz Saki Shi, Meilin Tian, Yuanyuan Wang, Sizhe Wang, Zhangyu Zalewski, Joseph Databases 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. |
| title | The KnowWhereGraph: A Large-Scale Geo-Knowledge Graph for Interdisciplinary Knowledge Discovery and Geo-Enrichment |
| topic | Databases |
| url | https://arxiv.org/abs/2502.13874 |