Corleone: hands-off crowdsourcing for entity matching

By Chaitanya S. Gokhale, Sanjib Das, AnHai Doan, Jeffrey F. Naughton, Narasimhan Rampalli, Jude W. Shavlik, Xiaojin Zhu. 2014

277 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Management Science and Operations Research, Information Extraction

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Citations in the corpus

AlphaClean: Automatic Generation of Data Cleaning Pipelines (2019, methodology): “Machine Learning in Data Cleaning: Machine learning has been widely used to improve the efficiency and/or reliability of data cleaning [19, 48, 49].”
A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, neutral): “Corleone [56] is a hands-off crowdsourcing system,”
BoostClean: Automated Error Detection and Repair for Machine Learning (2017, methodology): “There are a number of other works that use machine learning to improve the efﬁciency and/or reliability of data cleaning [21,51,52].”
Toward a System Building Agenda for Data Integration (and Data Science) (2018, methodology): “This is based on our earlier Corleone/Falcon work [13, 7].”
Trends in Cleaning Relational Data: Consistency and Deduplication (2015, methodology): “In contrast, deciding whether two records are duplicates usually requires fuzzy matching, for which humans sometimes can achieve better accuracy [114, 59, 116, 112].”
Wisteria: Nurturing Scalable Data Cleaning Infrastructure (2015, methodology): “In this paper, we introduce Wisteria, a system designed to support the iterative development and optimization of data cleaning plans end to end.”
ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models (2016, methodology): “Efﬁciency: Conversely, hypothetically assume that the analyst has implemented a correct Update ( · ) primitive and implements Identify ( · ) with a technique such as Active Learning to select records to clean [37, 38, 16].”
Distributed Representations of Tuples for Entity Resolution (2017, background): “Recently, ER using crowdsourcing has become popular [50, 25].”
Crowdsourced Data Management: Industry and Academic Perspectives (2015, methodology): “This work has focused primarily on combining machine learning methods with crowdsourcing; crowdsourcing is typically used for verifying predicted matches [189, 68, 85]; in some cases, it is also used to provide training data for matching algorithms [138, 192, 85].”
Deep Learning for Entity Matching: A Design Space Exploration (2018, methodology): “Finally, a different line of work develops methods to leverage human experts [26, 72, 81] to guide EM and help refine the learned matching functions.”
Crowdsourced Data Management: A Survey (2016, methodology): “, task design [79], [118], [126], leveraging transitive relations [48], [116], [121], [122], and task selection [45], [59], [82], [114], [125].”
Big data integration (2013, unknown): “r′232 r′231 r′233 (49) r′214 r′232 r′231 r′233 (S1) r′221 r′223 r′222 r′224 (SFO, 2014-03-09)”
SampleClean: Fast and Reliable Analytics on Dirty Data (2015, background): “While crowdsourcing is an increasingly viable option for correcting some types of errors [29, 22, 48, 24, 38, 4, 14], it comes at the signiﬁcant cost of additional latency and the overhead of managing human workers.”
A Data Quality Metric (DQM): How to Estimate the Number of Undetected Errors in Data Sets (2016, methodology): “The product data set [32, 14] contains 2336 records by Amazon and 1363 records by Google of the form: Product ( retailer, id, name 1 , name 2 , vendor, price ) Each row belongs to either Amazon or Google ( retailer = { Amazon, Google } ), and the size of possible pairs is 1363 × 2336 .”