Enhancing answer completeness of SPARQL queries via crowdsourcing
- Submitting institution
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King's College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 126761907
- Type
- D - Journal article
- DOI
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10.1016/j.websem.2017.07.001
- Title of journal
- Journal of Web Semantics
- Article number
- -
- First page
- 41
- Volume
- 45
- Issue
- -
- ISSN
- 1570-8268
- Open access status
- Technical exception
- Month of publication
- July
- Year of publication
- 2017
- URL
-
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- 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
- 7
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- First human-in-the-loop system for answering queries against linked-data databases through paid microtask crowdsourcing. Extension of KCAP2015 paper with best-student paper award (https://twitter.com/maribelacosta/status/652907907764363265/photo/1). Starting point for H2020 Qrowd (732194), which extended the idea as follows: (i) it developed a full human-in-the-loop linked-data technology stack around the original query answering system; (ii) the human-in-the-loop element used multiple forms of crowdsourcing rather than just microtasks. The technology was deployed in an innovation lab (https://www.comune.trento.it/Progetti/Qrowd) in Trento, Italy, which engaged 200+ citizens in collecting 8 high-value government datasets, achieving 25% lower costs than traditional survey methodologies. Recognised in 2020 BDVA Success Story competition (https://twitter.com/BDVA_PPP/status/1315897161696137216).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -