User-generated content, such as photos and videos, is often annotated by users with free-text labels, called tags. Increasingly, such content is also georeferenced, i.e., it is associated with geographic coordinates. The implicit relationships between tags and their locations can tell us much about how people conceptualize places and relations between them. However, extracting such knowledge from social annotations presents many challenges, since annotations are often ambiguous, noisy, uncertain and spatially inhomogeneous. We introduce a probabilistic framework for modeling georeferenced annotations and a method for learning model parameters from data. The framework is flexible and general, and can be used in a variety of applications that mine geospatial knowledge from user-generated content. Specifically, we study three problems: extracting place semantics, predicting locations of photos and learning part-of relations between places. We show our method performs well compared to state-of-the-art approaches developed for the first two problems, and offers a novel solution to the problem of learning relations between places.
['Suradej Intagorn', 'Kristina Lerman']
A probabilistic approach to mining geospatial knowledge from social annotations