Reasoning over higher-order qualitative spatial relations via spatially explicit neural networks

Qualitative spatial reasoning has been a core research topic in GIScience and AI for decades. It has been adopted in a wide range of applications such as wayfinding, question answering, and robotics. Most developed spatial inference engines use symbolic representation and reasoning, which focuses on...

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Bibliographic Details
Published in:	International journal of geographical information science : IJGIS 2022-11, Vol.36 (11), p.2194-2225
Main Authors:	Zhu, Rui, Janowicz, Krzysztof, Cai, Ling, Mai, Gengchen
Format:	Article
Language:	eng
Subjects:	GeoAI Geospatial knowledge graphs higher-order spatial interactions qualitative spatial representation and reasoning spatially explicit methods
Online Access:	Get full text
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Summary:	Qualitative spatial reasoning has been a core research topic in GIScience and AI for decades. It has been adopted in a wide range of applications such as wayfinding, question answering, and robotics. Most developed spatial inference engines use symbolic representation and reasoning, which focuses on small and densely connected data sets, and struggles to deal with noise and vagueness. However, with more sensors becoming available, reasoning over spatial relations on large-scale and noisy geospatial data sets requires more robust alternatives. This paper, therefore, proposes a subsymbolic approach using neural networks to facilitate qualitative spatial reasoning. More specifically, we focus on higher-order spatial relations as those have been largely ignored due to the binary nature of the underlying representations, e.g. knowledge graphs. We specifically explore the use of neural networks to reason over ternary projective relations such as between. We consider multiple types of spatial constraint, including higher-order relatedness and the conceptual neighborhood of ternary projective relations to make the proposed model spatially explicit. We introduce evaluating results demonstrating that the proposed spatially explicit method substantially outperforms the existing baseline by about 20%.
ISSN:	1365-8816 1365-8824