  The linked open data (LOD) paradigm has emerged as a promising approach to
structuring and sharing geospatial information. One of the major obstacles to
this vision lies in the difficulties found in the automatic integration between
heterogeneous vocabularies and ontologies that provides the semantic backbone
of the growing constellation of open geo-knowledge bases. In this article, we
show how to utilize WordNet as a semantic hub to increase the integration of
LOD. With this purpose in mind, we devise Voc2WordNet, an unsupervised mapping
technique between a given vocabulary and WordNet, combining intensional and
extensional aspects of the geographic terms. Voc2WordNet is evaluated against a
sample of human-generated alignments with the OpenStreetMap (OSM) Semantic
Network, a crowdsourced geospatial resource, and the GeoNames ontology, the
vocabulary of a large digital gazetteer. These empirical results indicate that
the approach can obtain high precision and recall.
