A Learning-Based Framework for Improving Querying on Web Interfaces of Curated Knowledge Bases
- Submitting institution
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The University of Huddersfield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 5
- Type
- D - Journal article
- DOI
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10.1145/3155806
- Title of journal
- ACM Transactions on Internet Technology
- Article number
- 35
- First page
- 1
- Volume
- 18
- Issue
- 3
- ISSN
- 1533-5399
- Open access status
- Compliant
- Month of publication
- February
- 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
- No
- Number of additional authors
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5
- Research group(s)
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-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in an ERA2010 A-rated ACM transactions journal, this work introduces a client-side caching framework, SECF, to improve the overall querying performance on the SPARQL Endpoints that are built upon curated knowledge bases on the Web. This new query result caching method has been demonstrated to outperform state-of-the-art methods, and led some of the authors to win a national competitive research grant for investigation of trust in the large and noisy Web funded by Australian Research Council (2020-2022) (https://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/DP200102298)
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -