Abstract

Knowledge produced online often comes in the form of free-text labels, known as tags, with which users annotate the content they create, such as photos and videos. 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 two problems --- extracting place …

Date

November 1, 2012

Authors

Suradej Intagorn, Kristina Lerman

Journal

SIGSPATIAL Special

Volume

4

Issue

3

Pages

2-7

Publisher

ACM