research-article

On active learning of record matching packages

Authors:

Michaela Götz, and

Raghav KaushikAuthors Info & Claims

SIGMOD '10: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data

June 2010

Pages 783 - 794

https://doi.org/10.1145/1807167.1807252

Published: 06 June 2010 Publication History

Abstract

We consider the problem of learning a record matching package (classifier) in an active learning setting. In active learning, the learning algorithm picks the set of examples to be labeled, unlike more traditional passive learning setting where a user selects the labeled examples. Active learning is important for record matching since manually identifying a suitable set of labeled examples is difficult. Previous algorithms that use active learning for record matching have serious limitations: The packages that they learn lack quality guarantees and the algorithms do not scale to large input sizes. We present new algorithms for this problem that overcome these limitations. Our algorithms are fundamentally different from traditional active learning approaches, and are designed ground up to exploit problem characteristics specific to record matching. We include a detailed experimental evaluation on realworld data demonstrating the effectiveness of our algorithms.

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Index Terms

On active learning of record matching packages
1. Information systems
  1. Data management systems
    1. Database management system engines

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cover image ACM Conferences

SIGMOD '10: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data

June 2010

1286 pages

ISBN:9781450300322

DOI:10.1145/1807167

General Chair:
Ahmed Elmagarmid
Purdue University, USA
,
Program Chair:
Divyakant Agrawal
University of California at Santa Barbara, USA

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 06 June 2010

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SIGMOD/PODS '10: International Conference on Management of Data

June 6 - 10, 2010

Indiana, Indianapolis, USA

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Cited By

Han YLi C(2024)Entity Matching by Pool-Based Active LearningElectronics10.3390/electronics1303055913:3(559)Online publication date: 30-Jan-2024
https://doi.org/10.3390/electronics13030559
Lu DHan GZhao YHan Q(2024)Review of Deep Learning-Based Entity Alignment MethodsGreen, Pervasive, and Cloud Computing10.1007/978-981-99-9893-7_5(61-71)Online publication date: 23-Jan-2024
https://doi.org/10.1007/978-981-99-9893-7_5
Fan WHan ZRen WWang DWang YXie MYan M(2023)Splitting Tuples of Mismatched EntitiesProceedings of the ACM on Management of Data10.1145/36267631:4(1-29)Online publication date: 12-Dec-2023
https://doi.org/10.1145/3626763
Fan WFu WJin RLiu MLu PTian C(2023)Making It Tractable to Catch Duplicates and Conflicts in GraphsProceedings of the ACM on Management of Data10.1145/35889401:1(1-28)Online publication date: 30-May-2023
https://doi.org/10.1145/3588940
Wu RBendeck AChu XHe Y(2023)Ground Truth Inference for Weakly Supervised Entity MatchingProceedings of the ACM on Management of Data10.1145/35887121:1(1-28)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.1145/3588712
Li SWu H(2023)Transformer-based Denoising Adversarial Variational Entity ResolutionJournal of Intelligent Information Systems10.1007/s10844-022-00773-x61:2(631-650)Online publication date: 17-Apr-2023
https://doi.org/10.1007/s10844-022-00773-x
Fan W(2022)Big graphsProceedings of the VLDB Endowment10.14778/3554821.355489915:12(3782-3797)Online publication date: 1-Aug-2022
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Jain ASarawagi SSen P(2022)Deep indexed active learning for matching heterogeneous entity representationsProceedings of the VLDB Endowment10.14778/3485450.348545515:1(31-45)Online publication date: 14-Jan-2022
https://dl.acm.org/doi/10.14778/3485450.3485455
Ilyas IRekatsinas T(2022)Machine Learning and Data Cleaning: Which Serves the Other?Journal of Data and Information Quality10.1145/350671214:3(1-11)Online publication date: 21-Jul-2022
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Binette OSteorts R(2022)(Almost) all of entity resolutionScience Advances10.1126/sciadv.abi80218:12Online publication date: 25-Mar-2022
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