  In geographic information science and semantics, the computation of semantic
similarity is widely recognised as key to supporting a vast number of tasks in
information integration and retrieval. By contrast, the role of geo-semantic
relatedness has been largely ignored. In natural language processing, semantic
relatedness is often confused with the more specific semantic similarity. In
this article, we discuss a notion of geo-semantic relatedness based on Lehrer's
semantic fields, and we compare it with geo-semantic similarity. We then
describe and validate the Geo Relatedness and Similarity Dataset (GeReSiD), a
new open dataset designed to evaluate computational measures of geo-semantic
relatedness and similarity. This dataset is larger than existing datasets of
this kind, and includes 97 geographic terms combined into 50 term pairs rated
by 203 human subjects. GeReSiD is available online and can be used as an
evaluation baseline to determine empirically to what degree a given
computational model approximates geo-semantic relatedness and similarity.
