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A Demonstration of PERC: Probabilistic Entity Resolution With Crowd Errors

Summary: PERC performs crowdsourced entity resolution under noisy, potentially malicious answers via an uncertain-graph model and maximum-likelihood clustering. Its reliability-driven question selection is threshold-free and error-rate agnostic, improving quality while reducing crowd cost. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
ha64f515c1978a3af
Venue
VLDB
Year
2018
Pagerank
4.9793485e-05
Overall Rank
12,235 | 17.74%
DOI
10.14778/3229863.3236225

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BibTeX Citation

@article{ke_vldb18,
        title = {{A Demonstration of PERC: Probabilistic Entity Resolution With Crowd Errors}},
        author = {Ke, Xiangyu and Teo, Michelle and Khan, Arijit and Yalavarthi, Vijaya Krishna},
        journal = {PVLDB},
        series = {{VLDB} '18},
        volume = {11},
        number = {12},
        pages = {1922--1925},
        doi = {10.14778/3229863.3236225},
        url = {https://doi.org/10.14778/3229863.3236225},
        year = {2018}
}

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Showing 7 of 7 cited papers.

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

Rank Cited Paper Year Venue Pagerank
871 Leveraging Transitive Relations for Crowdsourced Joins 2013 SIGMOD 0.00013343705
933 Question Selection for Crowd Entity Resolution 2013 VLDB 0.00013004422
1,264 CDAS: A Crowdsourcing Data Analytics System 2012 VLDB 0.0001128316
1,472 Crowdsourcing Algorithms for Entity Resolution 2014 VLDB 0.00010558263
3,634 NADEEF/ER: Generic and Interactive Entity Resolution 2014 SIGMOD 7.1482079e-05
4,051 Crowd-Based Deduplication: An Adaptive Approach 2015 SIGMOD 6.8285415e-05
5,512 KATARA: Reliable Data Cleaning with Knowledge Bases and Crowdsourcing 2015 VLDB 6.0991137e-05
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