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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2405.07664 |
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| _version_ | 1866909200932667392 |
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| author | Zhu, Rui |
| author_facet | Zhu, Rui |
| contents | Geospatial knowledge graphs have emerged as a novel paradigm for representing and reasoning over geospatial information. In this framework, entities such as places, people, events, and observations are depicted as nodes, while their relationships are represented as edges. This graph-based data format lays the foundation for creating a "FAIR" (Findable, Accessible, Interoperable, and Reusable) environment, facilitating the management and analysis of geographic information. This entry first introduces key concepts in knowledge graphs along with their associated standardization and tools. It then delves into the application of knowledge graphs in geography and environmental sciences, emphasizing their role in bridging symbolic and subsymbolic GeoAI to address cross-disciplinary geospatial challenges. At the end, new research directions related to geospatial knowledge graphs are outlined. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07664 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Geospatial Knowledge Graphs Zhu, Rui Artificial Intelligence Geospatial knowledge graphs have emerged as a novel paradigm for representing and reasoning over geospatial information. In this framework, entities such as places, people, events, and observations are depicted as nodes, while their relationships are represented as edges. This graph-based data format lays the foundation for creating a "FAIR" (Findable, Accessible, Interoperable, and Reusable) environment, facilitating the management and analysis of geographic information. This entry first introduces key concepts in knowledge graphs along with their associated standardization and tools. It then delves into the application of knowledge graphs in geography and environmental sciences, emphasizing their role in bridging symbolic and subsymbolic GeoAI to address cross-disciplinary geospatial challenges. At the end, new research directions related to geospatial knowledge graphs are outlined. |
| title | Geospatial Knowledge Graphs |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2405.07664 |