KLog: A language for logical and relational learning with kernels
- Submitting institution
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University of Exeter
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1788
- Type
- D - Journal article
- DOI
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10.1016/j.artint.2014.08.003
- Title of journal
- Artificial Intelligence
- Article number
- -
- First page
- 117
- Volume
- 217
- Issue
- -
- ISSN
- 0004-3702
- Open access status
- Out of scope for open access requirements
- Month of publication
- August
- Year of publication
- 2014
- 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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3
- Research group(s)
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-
- Citation count
- 12
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- We introduce a novel approach to statistical relational learning, combining the flexibility of logic programming with robustness, high performance and efficiency of graph kernels achieving results that are more accurate and orders of magnitude faster than relational learning alternatives e.g., Tilde or Alchemy. Applications that use our approach include natural language and image understanding (10.1007/978-3-642-34166-3_19), human activity analysis (10.1016/j.artint.2018.12.005), semantic classification on building structures (10.1109/ICRA.2017.7989298). A derivation of the ideas developed here has been applied to problems in bioinformatics and has been instrumental in gaining the following grants from the German Research Foundation: BA 2168/4-3, SPP 1395 InKoMBio, BA 2168/4-2.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -