Enhancing answer completeness of SPARQL queries via crowdsourcing
- Submitting institution
-
University of Southampton
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 26089462
- Type
- D - Journal article
- DOI
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10.1016/j.websem.2017.07.001
- Title of journal
- Web Semantics
- Article number
- -
- First page
- 41
- Volume
- 45
- Issue
- -
- ISSN
- 1570-8268
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2017
- 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
- 7
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- First system for answering hybrid, human-machine queries against linked-data graph databases, extension of conference paper with best-student paper award at KCAP2015 (https://www.k-cap.org/kcap15, http://tiny.cc/7p6ejz). Pivotal to H2020 Qrowd, which built a full human-machine technology stack for linked-data analytics under Simperl's leadership (732194, 651505€ for Southampton). In Qrowd we set up a socio-technical lab (http://tiny.cc/7p6ejz) together with ATOS (Tomas Pariente, Head of Knowledge Lab, tomas.parientelobo@atos.net) and the City Council of Trento, Italy (Giacomo Fioroni, Head of Smart City Office, giacomo_fioroni@comune.trento.it). Through 2018/2019 it collected and analysed high-value open-government datasets about local transport, at a fraction of the costs of traditional methodologies.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -