Du, H orcid.org/0000-0002-6300-3503 and Alechina, N (2016) Qualitative Spatial Logics for Buffered Geometries. Journal of Artificial Intelligence Research, 56. pp. 693-745. ISSN 1943-5037
Abstract
This paper describes a series of new qualitative spatial logics for checking consistency of sameAs and partOf matches between spatial objects from different geospatial datasets, especially from crowd-sourced datasets. Since geometries in crowd-sourced data are usually not very accurate or precise, we buffer geometries by a margin of error or a level of tolerance, and define spatial relations for buffered geometries. The spatial logics formalize the notions of `buffered equal' (intuitively corresponding to `possibly sameAs'), `buffered part of' (`possibly partOf'), `near' (`possibly connected') and `far' (`definitely disconnected'). A sound and complete axiomatisation of each logic is provided with respect to models based on metric spaces. For each of the logics, the satisfiability problem is shown to be NP-complete. Finally, we briefly describe how the logics are used in a system for generating and debugging matches between spatial objects, and report positive experimental evaluation results for the system.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2016 , Association for the Advancement of Artificial Intelligence. This is an author produced version of a paper published in . Uploaded in accordance with the publisher's self-archiving policy |
Keywords: | qualitative spatial logic; geospatial data matching; soundness; completeness; decidability; complexity |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence & Biological Systems (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Sep 2016 10:01 |
Last Modified: | 21 Jan 2018 19:15 |
Published Version: | http://dx.doi.org/10.1613/jair.5140 |
Status: | Published |
Publisher: | Association for the Advancement of Artificial Intelligence |
Identification Number: | 10.1613/jair.5140 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:104356 |