Learning Expressive Linkage Rules using Genetic Programming
Summary: GenLink uses genetic programming to learn expressive entity-resolution rules from reference links, jointly discovering properties, transformation chains, distances, thresholds, and nonlinear comparison aggregation. It matches human-written-rule accuracy and improves on prior GP methods. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Robert Isele (Freie University of Berlin)
- 2. Christian Bizer (Freie University of Berlin)
BibTeX Citation
@article{isele_vldb12,
title = {{Learning Expressive Linkage Rules using Genetic Programming}},
author = {Isele, Robert and Bizer, Christian},
journal = {PVLDB},
series = {{VLDB} '12},
volume = {5},
number = {11},
pages = {1638--1649},
doi = {10.14778/2350229.2350270},
url = {https://doi.org/10.14778/2350229.2350270},
year = {2012}
}
Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,623 | The return of JedAI: End-to-End Entity Resolution for Structured and Semi-Structured Data | 2018 | VLDB | 8.3327284e-05 |
| 3,746 | Discovering Linkage Points over Web Data | 2013 | VLDB | 7.1561558e-05 |
| 4,056 | A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs | 2020 | VLDB | 6.9344762e-05 |
| 5,519 | Repairing Vertex Labels under Neighborhood Constraints | 2014 | VLDB | 6.1885644e-05 |
| 9,610 | The Battleship Approach to the Low Resource Entity Matching Problem | 2023 | SIGMOD | 5.2460348e-05 |
| 11,388 | Matching Roles from Temporal Data | 2023 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 528 | On Active Learning of Record Matching Packages | 2010 | SIGMOD | 0.00017100838 |
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