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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)

Paper ID
10586
Venue
VLDB
Year
2012
Pagerank
7.1496482e-05
Overall Rank
3,755 | 74.24%
DOI
10.14778/2350229.2350270

Incoming Non-self Citations Over Time

Authors

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)

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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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