TY - GEN
T1 - Assessing similarities of qualitative spatio-temporal relations
AU - Klippel, Alexander
AU - Yang, Jinlong
AU - Wallgrün, Jan Oliver
AU - Dylla, Frank
AU - Li, Rui
PY - 2012
Y1 - 2012
N2 - In this article we analyze behavioral data to advance knowledge on how to assess similarities of events and spatial relations characterized by qualitative spatial calculi. We have collected a large amount of behavioral data evaluating topological relations specified in the Region Connection Calculus and Intersection Models. Several suggestions have been made in the literature on how to use associated conceptual neighborhood graphs to assess the similarities between events and static spatial relations specified within these frameworks. However, to the best of our knowledge, there are few (to none) approaches that use behavioral data to formally assess similarities. This article is contributing to this endeavor of using behavioral data as a basis for similarities (and associated weights) by (a) discussing a number of approaches that allow for transforming behavioral data into numeric values; (b) applying these approaches to nine data sets we collected in the last couple of years on conceptualizing spatio-temporal information using RCC/IM as a baseline; and (c) discussing potential weighting schemes but also revealing essential avenues for future research.
AB - In this article we analyze behavioral data to advance knowledge on how to assess similarities of events and spatial relations characterized by qualitative spatial calculi. We have collected a large amount of behavioral data evaluating topological relations specified in the Region Connection Calculus and Intersection Models. Several suggestions have been made in the literature on how to use associated conceptual neighborhood graphs to assess the similarities between events and static spatial relations specified within these frameworks. However, to the best of our knowledge, there are few (to none) approaches that use behavioral data to formally assess similarities. This article is contributing to this endeavor of using behavioral data as a basis for similarities (and associated weights) by (a) discussing a number of approaches that allow for transforming behavioral data into numeric values; (b) applying these approaches to nine data sets we collected in the last couple of years on conceptualizing spatio-temporal information using RCC/IM as a baseline; and (c) discussing potential weighting schemes but also revealing essential avenues for future research.
UR - http://www.scopus.com/inward/record.url?scp=84868224782&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84868224782&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-32732-2_17
DO - 10.1007/978-3-642-32732-2_17
M3 - Conference contribution
AN - SCOPUS:84868224782
SN - 9783642327315
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 242
EP - 261
BT - Spatial Cognition VIII - International Conference, Spatial Cognition 2012, Proceedings
T2 - International Conference on Spatial Cognition, SC 2012
Y2 - 31 August 2012 through 3 September 2012
ER -