  Representations of geographic entities captured in popular knowledge graphs
such as Wikidata and DBpedia are often incomplete. OpenStreetMap (OSM) is a
rich source of openly available, volunteered geographic information that has a
high potential to complement these representations. However, identity links
between the knowledge graph entities and OSM nodes are still rare. The problem
of link discovery in these settings is particularly challenging due to the lack
of a strict schema and heterogeneity of the user-defined node representations
in OSM. In this article, we propose OSM2KG - a novel link discovery approach to
predict identity links between OSM nodes and geographic entities in a knowledge
graph. The core of the OSM2KG approach is a novel latent, compact
representation of OSM nodes that captures semantic node similarity in an
embedding. OSM2KG adopts this latent representation to train a supervised model
for link prediction and utilises existing links between OSM and knowledge
graphs for training. Our experiments conducted on several OSM datasets, as well
as the Wikidata and DBpedia knowledge graphs, demonstrate that OSM2KG can
reliably discover identity links. OSM2KG achieves an F1 score of 92.05% on
Wikidata and of 94.17% on DBpedia on average, which corresponds to a 21.82
percentage points increase in F1 score on Wikidata compared to the best
performing baselines.
