  OpenStreetMap (OSM) is one of the richest openly available sources of
volunteered geographic information. Although OSM includes various geographical
entities, their descriptions are highly heterogeneous, incomplete, and do not
follow any well-defined ontology. Knowledge graphs can potentially provide
valuable semantic information to enrich OSM entities. However, interlinking OSM
entities with knowledge graphs is inherently difficult due to the large,
heterogeneous, ambiguous, and flat OSM schema and the annotation sparsity. This
paper tackles the alignment of OSM tags with the corresponding knowledge graph
classes holistically by jointly considering the schema and instance layers. We
propose a novel neural architecture that capitalizes upon a shared latent space
for tag-to-class alignment created using linked entities in OSM and knowledge
graphs. Our experiments performed to align OSM datasets for several countries
with two of the most prominent openly available knowledge graphs, namely,
Wikidata and DBpedia, demonstrate that the proposed approach outperforms the
state-of-the-art schema alignment baselines by up to 53 percentage points in
terms of F1-score. The resulting alignment facilitates new semantic annotations
for over 10 million OSM entities worldwide, which is more than a 400% increase
compared to the existing semantic annotations in OSM.
