Bottom-up gazetteers: Learning from the implicit semantics of geotags

As directories of named places, gazetteers link the names to geographic footprints and place types. Most existing gazetteers are managed strictly top-down: entries can only be added or changed by the responsible toponymic authority. The covered vocabulary is therefore often limited to an administrative view on places, using only official place names. In this paper, we propose a bottom-up approach for gazetteer building based on geotagged photos harvested from the web. We discuss the building blocks of a geotag and how they relate to each other to formally define the notion of a geotag. Based on this formalization, we introduce an extraction process for gazetteer entries that captures the emergent semantics of collections of geotagged photos and provides a group-cognitive perspective on named places. Using an experimental setup based on clustering and filtering algorithms, we demonstrate how to identify place names and assign adequate geographic footprints. The results for three different place names (Soho, Camino de Santiago and Kilimanjaro), representing different geographic feature types, are evaluated and compared to the results obtained from traditional gazetteers. Finally, we sketch how our approach can be combined with other (for example, linguistic) approaches and discuss how such a bottom-up gazetteer can complement existing gazetteers.