2000
DOI: 10.1016/s0165-0114(99)00017-2
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Individual and multipersonal fuzzy spatial relations acquired using human–machine interaction

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Cited by 50 publications
(36 citation statements)
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“…Of particular relevance to our task of generating locational expressions in specific geographic (as opposed to "table top") contexts are a number of empirical, human-subject studies of the use of vague spatial language concepts that have been concerned with the possibility of fitting models to the experimental data. For example, Robinson conducted studies to acquire fuzzy membership functions to represent the concept of nearness [20] [21] with regard to the relationship between settlements that were mostly tens of kms apart. Using a system that learnt the fuzzy membership function, the subjects were asked to specify the truth or falsehood of nearness for specific instances of pairs of settlements, one of which was the ground location.…”
Section: Modelling the Applicability Of Spatial Prepositionsmentioning
confidence: 99%
See 1 more Smart Citation
“…Of particular relevance to our task of generating locational expressions in specific geographic (as opposed to "table top") contexts are a number of empirical, human-subject studies of the use of vague spatial language concepts that have been concerned with the possibility of fitting models to the experimental data. For example, Robinson conducted studies to acquire fuzzy membership functions to represent the concept of nearness [20] [21] with regard to the relationship between settlements that were mostly tens of kms apart. Using a system that learnt the fuzzy membership function, the subjects were asked to specify the truth or falsehood of nearness for specific instances of pairs of settlements, one of which was the ground location.…”
Section: Modelling the Applicability Of Spatial Prepositionsmentioning
confidence: 99%
“…Knowledge of the applicability of different prepositions was acquired through a set of human subject experiments conducted in a lab and online, in which participants were asked to rate the suitability of a set of prepositions (based on the prior caption analysis) to particular configurations of the located object and a reference location (<toponym>) to which it is related by the preposition. These experiments were similar to those of for example Worboys [32] and Robinson [20] [21]. They differ though in that the subjects were told the context of the task was photo captioning, the scale of map data was adapted to the typical scale found in the caption analysis experiments and the subjects were asked to provide ratings of applicability of given prepositions using values on a Likert scale from 1 to 9.…”
Section: Introductionmentioning
confidence: 99%
“…A number of fuzzy models have been devised for dealing with topological reasoning (Schneider (2001);Winter (2000)), geomorphological reasoning (Fisher et al (2004)), general spatial representations (Hwang and Thill (2005);Tang (2004) ;Pfoser et al (2005); Wang and Hall (1996)) and most importantly linguistic reasoning (Schockaert et al (2008); Gapp (1994); Robinson (2000); Worboys (2001) ;Worboys et al (2004); Gahegan (1995) ;Fuhr et al (1995)). The work by Schockaert et al (2008) illustrates that by using a focused corpus (in their case of hotel websites) it is possible to derive a fuzzy representation of an arbitrary spatial phrase such as "within walking distance", an approach that was used to derive an initial overview of how spatial prepositions were used in image captions (Hall and Jones (2008)).…”
Section: Fuzzy Models For Vaguenessmentioning
confidence: 99%
“…Initial approaches to representing vague spatial information computationally were the broad-boundary models (Cohn and Gotts (1996a); Clementini and Felice (1996)). Later fuzzy models were proposed as representations of vague spatial information (Altman (1994);Fisher (2000); Robinson (2000); Schneider (2000)) in order to overcome the simplifications of the broad-boundary models and create a more realistic model of the vague spatial information, but bring with them the problem of how to define the fuzzy membership function (Robinson (2003)). …”
Section: Vague Field Modelmentioning
confidence: 99%
“…Other context dependencies are discussed, but not implemented into the model; in particular, the attractiveness of objects (e.g., 1 km from a shop may be far, but 1 km from a toxic waste dump very near) and reachability. Finally, [203] deals with the construction of fuzzy sets for concepts such as near and far, by asking the user a series of questions of the form Do you consider x to be far from y, which have to be answered by either yes or no (x and y are cities, and users are given a map to answer the questions). The goal is to allow for flexible querying in GIS systems, by using membership definitions of vague nearness relations that correspond to the interpretation of these concepts by the user.…”
Section: Fuzzification Of Spatial Relationsmentioning
confidence: 99%