Large scale distributed spatio-temporal reasoning using real-world knowledge graphs
- Submitting institution
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The University of Huddersfield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 42
- Type
- D - Journal article
- DOI
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10.1016/j.knosys.2018.08.035
- Title of journal
- Knowledge-Based Systems
- Article number
- -
- First page
- 214
- Volume
- 163
- Issue
- -
- ISSN
- 0950-7051
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
-
-
- Citation count
- 4
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in a Scimago Q1 journal, this paper describes PARQR, a reasoner that takes a novel approach to the problem of reasoning over large scale spatial-temporal networks. Unlike existing techniques, PARQR uses parallel, distributed algorithms. The paper provides a comprehensive empirical evaluation where PARQR outperforms existing reasoners. The paper is cited as making a significant contribution towards solving the problem of large scale qualitative spatial-temporal reasoning e.g. Towards Parallelisation of Qualitative Spatial and Temporal Reasoning (Sioutis and Wolter, 2019) "[PARQR] is tailored for handling constraint networks consisting of millions of relations that cannot be tackled with other state-of-the-art approaches".
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -