Standpoint semantics for polysemy in spatial prepositions

2020 ◽  
Vol 30 (2) ◽  
pp. 635-661
Author(s):  
Edilson J Rodrigues ◽  
Paulo E Santos ◽  
Marcos Lopes ◽  
Brandon Bennett ◽  
Paul E Oppenheimer
Keyword(s):  
Spatial Reasoning ◽  
Semantic Features ◽  
Qualitative Spatial Reasoning ◽  
Spatial Prepositions ◽  
Order Language ◽  
Spatial Expressions ◽  
Computational Implementation ◽  
Image Schema ◽  
Spatial Axis

Abstract In this paper, we present a formalism for handling polysemy in spatial expressions based on supervaluation semantics called standpoint semantics for polysemy (SSP). The goal of this formalism is, given a prepositional phrase, to define its possible spatial interpretations. For this, we propose to characterize spatial prepositions by means of a triplet $\langle $image schema, semantic feature, spatial axis$\rangle $. The core of SSP is predicate grounding theories, which are formulas of a first-order language that define a spatial preposition through the semantic features of its trajector and landmark. Precisifications are also established, which are a set of formulae of a qualitative spatial reasoning formalism that aims to provide the spatial characterization of the trajector with respect to the landmark. In addition to the theoretical model, we also present results of a computational implementation of SSP for the preposition ‘in’.

scholarly journals INTERSECTION DETECTION BASED ON QUALITATIVE SPATIAL REASONING ON STOPPING POINT CLUSTERS

2016 ◽  
Vol XLI-B2 ◽  
pp. 269-276
Author(s):  
S. Zourlidou ◽  
M. Sester
Keyword(s):  
Spatial Reasoning ◽  
Turning Points ◽  
Relational Reasoning ◽  
Semantic Features ◽  
Qualitative Spatial Reasoning ◽  
The Road ◽  
Making Sense ◽  
Intersection Detection ◽  
Moving Vehicles ◽  
Representative Points

The purpose of this research is to propose and test a method for detecting intersections by analysing collectively acquired trajectories of moving vehicles. Instead of solely relying on the geometric features of the trajectories, such as heading changes, which may indicate turning points and consequently intersections, we extract semantic features of the trajectories in form of sequences of <i>stops</i> and <i>moves</i>. Under this spatiotemporal prism, the extracted semantic information which indicates where vehicles stop can reveal important locations, such as junctions. The advantage of the proposed approach in comparison with existing turning-points oriented approaches is that it can detect intersections even when not all the crossing road segments are sampled and therefore no turning points are observed in the trajectories. The challenge with this approach is that first of all, not all vehicles stop at the same location – thus, the stop-location is blurred along the direction of the road; this, secondly, leads to the effect that nearby junctions can induce similar stop-locations. As a first step, a density-based clustering is applied on the layer of stop observations and clusters of stop events are found. Representative points of the clusters are determined (one per cluster) and in a last step the existence of an intersection is clarified based on spatial relational cluster reasoning, with which less informative geospatial clusters, in terms of whether a junction exists and where its centre lies, are transformed in more informative ones. Relational reasoning criteria, based on the relative orientation of the clusters with their adjacent ones are discussed for making sense of the relation that connects them, and finally for forming groups of stop events that belong to the same junction.

scholarly journals INTERSECTION DETECTION BASED ON QUALITATIVE SPATIAL REASONING ON STOPPING POINT CLUSTERS

2016 ◽  
Vol XLI-B2 ◽  
pp. 269-276 ◽  
Author(s):  
S. Zourlidou ◽  
M. Sester
Keyword(s):  
Spatial Reasoning ◽  
Turning Points ◽  
Relational Reasoning ◽  
Semantic Features ◽  
Qualitative Spatial Reasoning ◽  
The Road ◽  
Making Sense ◽  
Intersection Detection ◽  
Moving Vehicles ◽  
Representative Points

The purpose of this research is to propose and test a method for detecting intersections by analysing collectively acquired trajectories of moving vehicles. Instead of solely relying on the geometric features of the trajectories, such as heading changes, which may indicate turning points and consequently intersections, we extract semantic features of the trajectories in form of sequences of &lt;i&gt;stops&lt;/i&gt; and &lt;i&gt;moves&lt;/i&gt;. Under this spatiotemporal prism, the extracted semantic information which indicates where vehicles stop can reveal important locations, such as junctions. The advantage of the proposed approach in comparison with existing turning-points oriented approaches is that it can detect intersections even when not all the crossing road segments are sampled and therefore no turning points are observed in the trajectories. The challenge with this approach is that first of all, not all vehicles stop at the same location – thus, the stop-location is blurred along the direction of the road; this, secondly, leads to the effect that nearby junctions can induce similar stop-locations. As a first step, a density-based clustering is applied on the layer of stop observations and clusters of stop events are found. Representative points of the clusters are determined (one per cluster) and in a last step the existence of an intersection is clarified based on spatial relational cluster reasoning, with which less informative geospatial clusters, in terms of whether a junction exists and where its centre lies, are transformed in more informative ones. Relational reasoning criteria, based on the relative orientation of the clusters with their adjacent ones are discussed for making sense of the relation that connects them, and finally for forming groups of stop events that belong to the same junction.

PLCA

2012 ◽  
pp. 63-96 ◽  
Author(s):  
Kazuko Takahashi
Keyword(s):  
Graph Theory ◽  
Computational Geometry ◽  
Spatial Data ◽  
Qualitative Data ◽  
Spatial Reasoning ◽  
Research Area ◽  
Qualitative Spatial Reasoning ◽  
Research Areas ◽  
Fruitful Research ◽  
Topology Graph

This chapter describes a framework called PLCA for Qualitative Spatial Reasoning (QSR) based on the connection patterns of regions. The goal of this chapter is to provide a simple but expressive and feasible representation for qualitative data with sufficient reasoning ability. PLCA provides a symbolic representation for spatial data using simple objects. The authors of this chapter define its expression and operations on it, and show the correspondance between the expression and a figure. PLCA also provides semantical reasoning incorporated with spatial reasoning. Moreover, it can be extended to handle shapes of regions. Throughout the study, the authors discovered many topics that relate QSR to other research areas such as topology, graph theory, and computational geometry, while achieving the research goals. This indicates that QSR is a very fruitful research area.

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