Paper Info
Title
Assessing similarities of qualitative spatio-temporal relations
Abstract
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.
Year
DOI
Venue
2012
10.1007/978-3-642-32732-2_17
Spatial Cognition
Keywords
Field
DocType
intersection models,behavioral data,region connection calculus,qualitative spatio-temporal relation,essential avenue,qualitative spatial calculus,large amount,spatial relation,static spatial relation,assessing similarity,conceptual neighborhood graph
Spatial relation,Graph,Data mining,Data set,Weighting,Spatial cognition,Behavioral data,Natural language processing,Artificial intelligence,Mathematics,Region connection calculus
Conference
Citations 
PageRank 
References 
1
0.47
21
Authors
5
Name
Order
Citations
PageRank
Alexander Klippel148349.70
Jinlong Yang272.59
Jan Oliver Wallgrün323319.29
Frank Dylla425220.20
Rui Li5283.41