Semantic-aware blocking for entity resolution
- Submitting institution
-
The University of Reading
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 78675
- Type
- D - Journal article
- DOI
-
10.1109/TKDE.2015.2468711
- Title of journal
- IEEE Transactions on Knowledge and Data Engineering
- Article number
- -
- First page
- 166
- Volume
- 28
- Issue
- 1
- ISSN
- 1558-2191
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2015
- 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
-
2
- Research group(s)
-
9 - DSAI
- Citation count
- 19
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper is the first work to propose a novel semantic-aware blocking framework for entity resolution. The proposed framework can support efficient similarity searches on records in both textual and semantic similarity spaces. The experimental results on two real-world datasets show that the proposed framework can significantly improve the quality of blocks, particularly when datasets are imperfect (i.e. contain inaccurate, incomplete or erroneous data). It has wide ranging application including in transportation, finance, law enforcement, and counter terrorism.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -