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Entity Matching Meets Data Science

  • Published:2019-06-25 Issue: Volume: Page:
  • ISSN:
  • Container-title:Proceedings of the 2019 International Conference on Management of Data
  • language:
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Author:

Govind Yash1,Konda Pradap2,Suganthan G.C. Paul3,Martinkus Philip1,Nagarajan Palaniappan4,Li Han1,Soundararajan Aravind1,Mudgal Sidharth4,Ballard Jeff R.1,Zhang Haojun1,Ardalan Adel1,Das Sanjib5,Paulsen Derek1,Singh Saini Amanpreet1,Paulson Erik6,Park Youngchoon6,Carter Marshall7,Sun Mingju7,Fung Glenn M.7,Doan AnHai1

Affiliation:

1. University of Wisconsin - Madison, Madison, WI, USA

2. Facebook, Madison, WI, USA

3. Google, Mountain View, CA, USA

4. Amazon, Madison, WI, USA

5. Google, Madison, WI, USA

6. Johnson Control, Madison, WI, USA

7. American Family Insurance, Madison, WI, USA

Publisher

ACM

Reference32 articles.

1. Magellan

2. Toward a system building agenda for Data Integration (and Data Science);Doan AnHai;IEEE Data Eng. Bull.,2018

Cited by 14 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Fairness-Aware Data Preparation for Entity Matching;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

2. Sparkly: A Simple yet Surprisingly Strong TF/IDF Blocker for Entity Matching;Proceedings of the VLDB Endowment;2023-02

3. Synthesizing Privacy Preserving Entity Resolution Datasets;2022 IEEE 38th International Conference on Data Engineering (ICDE);2022-05

4. Deep entity matching with adversarial active learning;The VLDB Journal;2022-04-28

5. (Almost) all of entity resolution;Science Advances;2022-03-25
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