  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.
