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Supervised Meta-blocking

Summary: Supervised meta-blocking learns classifiers to prune entity-resolution comparisons, beating coarse pruning. Compact feature set, low extraction cost, strong discrimination; effective with small training data; evaluated on 10 real/synthetic datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11077
Venue
VLDB
Year
2014
Pagerank
6.4338718e-05
Overall Rank
4,938 | 66.13%
DOI
10.14778/2733085.2733098

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{papadakis_vldb14,
        title = {{Supervised Meta-blocking}},
        author = {Papadakis, George and Papastefanatos, George and Koutrika, Georgia},
        journal = {PVLDB},
        series = {{VLDB} '14},
        volume = {7},
        number = {14},
        pages = {1929--1932},
        doi = {10.14778/2733085.2733098},
        url = {https://doi.org/10.14778/2733085.2733098},
        year = {2014}
}

Incoming Citations (Sorted by Pagerank)

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Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
58 The Merge/Purge Problem for Large Databases 1995 SIGMOD 0.00040116748
107 Approximate String Joins in a Database (Almost) for Free 2001 VLDB 0.00033511706
1,162 Principles of Dataspace Systems 2006 PODS 0.00011866011
1,293 Entity Resolution with Iterative Blocking 2009 SIGMOD 0.00011292804
5,830 Exploiting Context Analysis for Combining Multiple Entity Resolution Systems 2009 SIGMOD 6.0734178e-05
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