Consequence-based and fixed-parameter tractable reasoning in description logics
- Submitting institution
-
University of Oxford
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1933
- Type
- D - Journal article
- DOI
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10.1016/j.artint.2014.01.002
- Title of journal
- Artificial Intelligence
- Article number
- -
- First page
- 29
- Volume
- 209
- Issue
- 1
- ISSN
- 0004-3702
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- 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
-
2
- Research group(s)
-
-
- Citation count
- 11
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper is among the first to analyse the parameterised complexity of description logic reasoning, helping to introduce a framework which has been built on in subsequent work (e.g. papers by Carral et al., and by de Haan, at KR 2018; Nalon, Dixon and Hustadt in ACM TOCL 2019). The techniques developed in the paper can predict and explain the performance of reasoning systems, which is important for practical applications: for example, they are relevant to the ELK reasoner, the de facto global standard for classifying SNOMED CT, a large clinical vocabulary, in health systems across the world.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -