Computational approaches to finding and measuring inconsistency in arbitrary knowledge bases
- Submitting institution
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University of Bristol
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 131542355
- Type
- D - Journal article
- DOI
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10.1016/j.ijar.2014.06.003
- Title of journal
- International Journal of Approximate Reasoning
- Article number
- -
- First page
- 1659
- Volume
- 55
- Issue
- 8
- ISSN
- 0888-613X
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2014
- 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)
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A - Artificial Intelligence and Autonomy
- Citation count
- 17
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Work presented in this paper is the first on both the implementation of measuring inconsistency in knowledgebases and the systematic evaluation of the effect of logical sentences on degrees of inconsistency. Its novelty lies in integrating state-of-the-art SAT solvers with inconsistency measures. It was born out of real-world applications (e.g., the IBM QRadar rule sets), with the tool (https://seis.bristol.ac.uk/~km17304/mimus/) applicable for validating any arbitrary knowledge-bases. The tool was released to the AI research community and has been used/extended by international researchers ((http://tweetyproject.org/w/incmes/). A main outcome of the £30m CSIT project with funding from EPSRC (EP/G034303/1, EP/H049606/1), TSB, industry and InvestNI
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -