Spatially aware term selection for geotagging

Van Laere, Olivier, Quinn, Jonathan Alexander, Schockaert, Steven ORCID: https://orcid.org/0000-0002-9256-2881 and Dhoedt, Bart 2014. Spatially aware term selection for geotagging. IEEE Transactions on Knowledge and Data Engineering 26 (1) , pp. 221-234. 10.1109/TKDE.2013.42

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Abstract

The task of assigning geographic coordinates to textual resources plays an increasingly central role in geographic information retrieval. The ability to select those terms from a given collection that are most indicative of geographic location is of key importance in successfully addressing this task. However, this process of selecting spatially relevant terms is at present not well understood, and the majority of current systems are based on standard term selection techniques, such as x2 or information gain, and thus fail to exploit the spatial nature of the domain. In this paper, we propose two classes of term selection techniques based on standard geostatistical methods. First, to implement the idea of spatial smoothing of term occurrences, we investigate the use of kernel density estimation (KDE) to model each term as a two-dimensional probability distribution over the surface of the Earth. The second class of term selection methods we consider is based on Ripley's K statistic, which measures the deviation of a point set from spatial homogeneity. We provide experimental results which compare these classes of methods against existing baseline techniques on the tasks of assigning coordinates to Flickr photos and to Wikipedia articles, revealing marked improvements in cases where only a relatively small number of terms can be selected.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1041-4347
Last Modified:	25 Oct 2022 09:42
URI:	https://orca.cardiff.ac.uk/id/eprint/59675

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