Details
| Original language | English |
|---|---|
| Title of host publication | CIKM 2021 - Proceedings of the 30th ACM International Conference on Information and Knowledge Management |
| Pages | 4604-4612 |
| Number of pages | 9 |
| ISBN (electronic) | 9781450384469 |
| Publication status | Published - 30 Oct 2021 |
| Event | 30th ACM International Conference on Information and Knowledge Management, CIKM 2021 - Virtual, Online, Australia Duration: 1 Nov 2021 → 5 Nov 2021 |
Publication series
| Name | International Conference on Information and Knowledge Management, Proceedings |
|---|
Abstract
OpenStreetMap (OSM) is currently the richest publicly available information source on geographic entities (e.g., buildings and roads) worldwide. However, using OSM entities in machine learning models and other applications is challenging due to the large scale of OSM, the extreme heterogeneity of entity annotations, and a lack of a well-defined ontology to describe entity semantics and properties. This paper presents GeoVectors - a unique, comprehensive world-scale linked open corpus of OSM entity embeddings covering the entire OSM dataset and providing latent representations of over 980 million geographic entities in 180 countries. The GeoVectors corpus captures semantic and geographic dimensions of OSM entities and makes these entities directly accessible to machine learning algorithms and semantic applications. We create a semantic description of the GeoVectors corpus, including identity links to the Wikidata and DBpedia knowledge graphs to supply context information. Furthermore, we provide a SPARQL endpoint - a semantic interface that offers direct access to the semantic and latent representations of geographic entities in OSM.
Keywords
- openstreetmap, OSM embeddings, semantic geographic data
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- General Business,Management and Accounting
- Decision Sciences(all)
- General Decision Sciences
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CIKM 2021 - Proceedings of the 30th ACM International Conference on Information and Knowledge Management. 2021. p. 4604-4612 (International Conference on Information and Knowledge Management, Proceedings).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - GeoVectors
T2 - 30th ACM International Conference on Information and Knowledge Management, CIKM 2021
AU - Tempelmeier, Nicolas
AU - Gottschalk, Simon
AU - Demidova, Elena
N1 - Funding Information: Acknowledgements. This work was partially funded by DFG, German Research Foundation (“WorldKG”, 424985896), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML”, 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand”, 01ME19009B), and the European Commission (EU H2020, “smashHit”, grant-ID 871477).
PY - 2021/10/30
Y1 - 2021/10/30
N2 - OpenStreetMap (OSM) is currently the richest publicly available information source on geographic entities (e.g., buildings and roads) worldwide. However, using OSM entities in machine learning models and other applications is challenging due to the large scale of OSM, the extreme heterogeneity of entity annotations, and a lack of a well-defined ontology to describe entity semantics and properties. This paper presents GeoVectors - a unique, comprehensive world-scale linked open corpus of OSM entity embeddings covering the entire OSM dataset and providing latent representations of over 980 million geographic entities in 180 countries. The GeoVectors corpus captures semantic and geographic dimensions of OSM entities and makes these entities directly accessible to machine learning algorithms and semantic applications. We create a semantic description of the GeoVectors corpus, including identity links to the Wikidata and DBpedia knowledge graphs to supply context information. Furthermore, we provide a SPARQL endpoint - a semantic interface that offers direct access to the semantic and latent representations of geographic entities in OSM.
AB - OpenStreetMap (OSM) is currently the richest publicly available information source on geographic entities (e.g., buildings and roads) worldwide. However, using OSM entities in machine learning models and other applications is challenging due to the large scale of OSM, the extreme heterogeneity of entity annotations, and a lack of a well-defined ontology to describe entity semantics and properties. This paper presents GeoVectors - a unique, comprehensive world-scale linked open corpus of OSM entity embeddings covering the entire OSM dataset and providing latent representations of over 980 million geographic entities in 180 countries. The GeoVectors corpus captures semantic and geographic dimensions of OSM entities and makes these entities directly accessible to machine learning algorithms and semantic applications. We create a semantic description of the GeoVectors corpus, including identity links to the Wikidata and DBpedia knowledge graphs to supply context information. Furthermore, we provide a SPARQL endpoint - a semantic interface that offers direct access to the semantic and latent representations of geographic entities in OSM.
KW - openstreetmap
KW - OSM embeddings
KW - semantic geographic data
UR - http://www.scopus.com/inward/record.url?scp=85119213256&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2108.13092
DO - 10.48550/arXiv.2108.13092
M3 - Conference contribution
AN - SCOPUS:85119213256
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 4604
EP - 4612
BT - CIKM 2021 - Proceedings of the 30th ACM International Conference on Information and Knowledge Management
Y2 - 1 November 2021 through 5 November 2021
ER -