Synthesizing Entity Matching Rules by Examples
Summary: Synthesizes entity-matching rules from labeled examples using program synthesis over a General Boolean Formula grammar, supporting arbitrary predicates and missing values. Produces compact, interpretable rules that beat shallow trees and rival opaque learners. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Rohit Singh (Massachusetts Institute of Technology; Uber)
- 2. Venkata Vamsikrishna Meduri (Arizona State University)
- 3. Ahmed Elmagarmid (Qatar Computing Research Institute)
- 4. Samuel Madden (Massachusetts Institute of Technology)
- 5. Paolo Papotti (EURECOM)
- 6. Jorge-Arnulfo Quiane-Ruiz (Qatar Computing Research Institute)
- 7. Armando Solar-Lezama (Massachusetts Institute of Technology)
- 8. Nan Tang (Qatar Computing Research Institute)
BibTeX Citation
@article{singh_vldb18,
title = {{Synthesizing Entity Matching Rules by Examples}},
author = {Singh, Rohit and Meduri, Venkata Vamsikrishna and Elmagarmid, Ahmed and Madden, Samuel and Papotti, Paolo and Quiane-Ruiz, Jorge-Arnulfo and Solar-Lezama, Armando and Tang, Nan},
journal = {PVLDB},
series = {{VLDB} '18},
volume = {11},
number = {2},
pages = {189--202},
doi = {10.14778/3149193.3149199},
url = {https://doi.org/10.14778/3149193.3149199},
year = {2018}
}
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