GeoVectors - Knowledge Graph (v1.0)

• Main Authors: Tempelmeier, Nicolas, Gottschalk, Simon, Demidova, Elena
• Format: Dataset
• Language: unknown
• Published: Zenodo 2021
• Subjects: Antarc*; Antarctica
• Online Access: https://dx.doi.org/10.5281/zenodo.4339523; https://zenodo.org/record/4339523

The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic dimensions of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic dimension of OpenStreetMap entities. The "-location" datasets provide the geographic dimension. This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors). This repository contains the GeoVectors Knowledge graph that models metadata of the embeddings and links to well-established sources such as Wikidata and DBpedia. The GeoVectors corpus is partitioned into regional subsets. The GeoVectors knowledge graph can be used to identify the subset that contains a particular linked entity. For further information, please visit http://geovectors.l3s.uni-hannover.de GeoVectors consists of the following subsets: Africa Africa. Tags: 10.5281/zenodo.4320881. Location: 10.5281/zenodo.4956827. Antarctica Antarctica. Tags: 10.5281/zenodo.4320869. Location: 10.5281/zenodo.4956951. Asia Asia. Tags: 10.5281/zenodo.4320895. Location: 10.5281/zenodo.4956955. Japan. Tags: 10.5281/zenodo.4320895. Location: 10.5281/zenodo.4957846. Indonesia. Tags: 10.5281/zenodo.4320895. Location: 10.5281/zenodo.4957818. Australia-Oceania Australia-Oceania. Tags: 10.5281/zenodo.4320963. Location: 10.5281/zenodo.4957176. Central-America Central-America. Tags: 10.5281/zenodo.4321010. Location: 10.5281/zenodo.4957278. Europe Europe-east. Tags: 10.5281/zenodo.4321012. Location: 10.5281/zenodo.4957475. Europe-west. Tags: 10.5281/zenodo.4321099. Location: 10.5281/zenodo.4957583. France. Tags: 10.5281/zenodo.4321153. Location: 10.5281/zenodo.4957689. Germany-nodes-relations. Tags: 10.5281/zenodo.4321406. Location: 10.5281/zenodo.4957746. Germany-ways. Tags: 10.5281/zenodo.4321420. Location: 10.5281/zenodo.4957746. Great-Britain. Tags: 10.5281/zenodo.4321175. Location: 10.5281/zenodo.4957805. Italy. Tags: 10.5281/zenodo.4321206. Location: 10.5281/zenodo.4957840. Netherlands. Tags: 10.5281/zenodo.4321252. Location: 10.5281/zenodo.4957583. Poland. Tags: 10.5281/zenodo.4321267. Location: 10.5281/zenodo.4957475. Russia. Tags: 10.5281/zenodo.4321358. Location: 10.5281/zenodo.4957903. North-America North-America. Tags: 10.5281/zenodo.4321449. Location: 10.5281/zenodo.4957873. US-Other. Tags: 10.5281/zenodo.4321762. Location: 10.5281/zenodo.4957931. US-South. Tags: 10.5281/zenodo.4321641. Location: 10.5281/zenodo.4957968. US-West. Tags: 10.5281/zenodo.4321708. Location: 10.5281/zenodo.4957931. South-America South-America. Tags: 10.5281/zenodo.4321635. Location: 10.5281/zenodo.4957911. Funding : This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), 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).