An unsupervised data-driven method to discover equivalent relations in large Linked Datasets
- Submitting institution
-
The University of Sheffield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2439
- Type
- D - Journal article
- DOI
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10.3233/SW-150193
- Title of journal
- Semantic Web
- Article number
- -
- First page
- 197
- Volume
- 8
- Issue
- 2
- ISSN
- 1570-0844
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- 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
-
4
- Research group(s)
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E - OAK
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work was the basis of an application developed for JustGiving, the largest donation company in the world. We mined large resources about charities and donation patterns in order to automatically categorise users, charities, fundraising pages and crowdfunding pages. This research was used to inform JustGiving’s GiveGraph (>0.5 billion elements) in order to better engage with their 20+ million users. Our work contributed to an increase in user engagement of 15% and an increased the probability by 378% that a user would visit a page when suggested. Contact: Former Lead Data and Machine Learning Engineer at JustGiving.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -