An Event-Based Framework for the Semantic Annotation of Locations

There is an increasing number of Linked Open Data sources that provide information about geographic locations, e.g., GeoNames or LinkedGeoData. There are also numerous data sources managing information about events, such as concerts or festivals. Suitably combining such sources would allow to answer queries such as ‘

When and where do live-concerts most likely occur in Munich?

’ or ‘

Are two locations similar in terms of their events?

’. Deriving correlations between geographic locations and event data, at different levels of abstraction, provides a semantically rich basis for location search, topic-based location clustering or recommendation services. However, little work has been done yet to extract such correlations from event datasets to annotate locations.

In this paper, we present an approach to the discovery of semantic annotations for locations from event data. We demonstrate the utility of extracted annotations in hierarchical clustering for locations, where the similarity between two locations is defined on the basis of their common event topics. To deal with periodic updates of event datasets, we furthermore give a scalable and efficient approach to incrementally update location annotations. To demonstrate the performance of our approach, we use real event datasets crawled from the Website

eventful.com

.