Datalog Rewritability of Disjunctive Datalog Programs and Non-Horn Ontologies
- Submitting institution
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University of Oxford
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1987
- Type
- D - Journal article
- DOI
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10.1016/j.artint.2016.03.006
- Title of journal
- Artificial Intelligence
- Article number
- -
- First page
- 90
- Volume
- 236
- Issue
- -
- ISSN
- 1872-7921
- Open access status
- Out of scope for open access requirements
- Month of publication
- March
- Year of publication
- 2016
- 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
- No
- Number of additional authors
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2
- Research group(s)
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-
- Citation count
- 11
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is the journal version of conference papers at AAAI�14 and RR�14. We study the problem of rewriting Disjunctive Datalog programs into plain Datalog programs (without disjunctions) that entail the same facts for every dataset. Datalog and its extensions provide the logical underpinning of rule-based languages for Knowledge Representation used in practice. The motivation for our rewriting techniques is to achieve tractable data complexity � a key theoretical requirement for data-intensive applications. Our article not only introduces novel rewriting techniques, but also precisely characterises the class of disjunctive programs that admit a rewriting. Our rewriting algorithms are also validated empirically.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
