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CrowdGame: A Game-Based Crowdsourcing System for Cost-Effective Data Labeling

Summary: CrowdGame generates candidate labeling rules and employs a game-based crowdsourcing loop to select high-coverage, high-accuracy rules, cutting labeling cost while preserving quality. A UI-enabled deployment applies these rules to entity matching and relation extraction, demonstrating scalable, cost-effective data labeling. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5729
Venue
SIGMOD
Year
2019
Pagerank
4.5429217e-05
Overall Rank
8,343 | 41.96%
DOI
10.1145/3299869.3320221

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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
254 Snorkel: Rapid Training Data Creation with Weak Supervision 2018 VLDB 0.00030540555
1,215 Snuba: Automating Weak Supervision to Label Training Data 2019 VLDB 0.0001323375
3,322 iCrowd: An Adaptive Crowdsourcing Framework 2015 SIGMOD 7.2230626e-05
6,868 Cost-Effective Data Annotation using Game-Based Crowdsourcing 2019 VLDB 4.9010083e-05
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