A Planet Scale Spatial-Temporal Knowledge Graph Based On OpenStreetMap And H3 Grid
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866917674211082240 |
|---|---|
| author | Böckling, Martin Paulheim, Heiko Detzler, Sarah |
| author_facet | Böckling, Martin Paulheim, Heiko Detzler, Sarah |
| contents | Geospatial data plays a central role in modeling our world, for which OpenStreetMap (OSM) provides a rich source of such data. While often spatial data is represented in a tabular format, a graph based representation provides the possibility to interconnect entities which would have been separated in a tabular representation. We propose in our paper a framework which supports a planet scale transformation of OpenStreetMap data into a Spatial Temporal Knowledge Graph. In addition to OpenStreetMap data, we align the different OpenStreetMap geometries on individual h3 grid cells. We compare our constructed spatial knowledge graph to other spatial knowledge graphs and outline our contribution in this paper. As a basis for our computation, we use Apache Sedona as a computational framework for our Spatial Temporal Knowledge Graph construction |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15375 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Planet Scale Spatial-Temporal Knowledge Graph Based On OpenStreetMap And H3 Grid Böckling, Martin Paulheim, Heiko Detzler, Sarah Artificial Intelligence Databases Distributed, Parallel, and Cluster Computing Geospatial data plays a central role in modeling our world, for which OpenStreetMap (OSM) provides a rich source of such data. While often spatial data is represented in a tabular format, a graph based representation provides the possibility to interconnect entities which would have been separated in a tabular representation. We propose in our paper a framework which supports a planet scale transformation of OpenStreetMap data into a Spatial Temporal Knowledge Graph. In addition to OpenStreetMap data, we align the different OpenStreetMap geometries on individual h3 grid cells. We compare our constructed spatial knowledge graph to other spatial knowledge graphs and outline our contribution in this paper. As a basis for our computation, we use Apache Sedona as a computational framework for our Spatial Temporal Knowledge Graph construction |
| title | A Planet Scale Spatial-Temporal Knowledge Graph Based On OpenStreetMap And H3 Grid |
| topic | Artificial Intelligence Databases Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2405.15375 |