Inconsistency Measures for Repair Semantics in OBDA
- Submitting institution
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University of Aberdeen
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 156137216
- Type
- E - Conference contribution
- DOI
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10.24963/ijcai.2018/273
- Title of conference / published proceedings
- Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
- First page
- 1977
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- July
- Year of publication
- 2018
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
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3
- Research group(s)
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-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work was developed in the French ANR AMANDE project which aim is to provide formal tools to create deliberation systems. The paper introduces novel query answering approaches for inconsistent knowledge bases based on inconsistency measures and provides both algorithms and theoretical properties. This work has received positive comments during the reviewing process in which they unanimously agreed that the results were significant and that this is the first framework for automatically ranking repairs using the structure of the underlying data. The method described was successfully applied and evaluated in the agribusiness EU-funded project Pack4Fresh (https://www.inrae.fr) to rank strawberry packaging.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
