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Entity Resolution (ER) aims to identify whether two tuples refer to the same real-world entity and is well-known to be labor-intensive.
The merge/purge problem for large databases
M. A. Hernández and S. J. Stolfo · 1995
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Large-scale deduplication with constraints using dedupalog
A. Arasu, C. Ré, and D. Suciu · 2009
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Reasoning about record matching rules
W. Fan, X. Jia, J. Li, and S. Ma · 2009
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Evaluation of entity resolution approaches on real-world match problems
H. Köpcke, A. Thor, and E. Rahm · 2010
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Human-powered sorts and joins
A. Marcus, E. Wu, D. R. Karger, S. Madden, and R. C. Miller · 2011
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Data Matching - Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection
P. Christen · 2012
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Foundations of Data Quality Management
W. Fan and F. Geerts · 2012
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Crowder: Crowdsourcing entity resolution
J. Wang, T. Kraska, M. J. Franklin, and J. Feng · 2012
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Distributed representations of words and phrases and their compositionality
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean · 2013
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Corleone: hands-off crowdsourcing for entity matching
C. Gokhale, S. Das, A. Doan, J. F. Naughton, N. Rampalli, J. W. Shavlik, and X. Zhu · 2014
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Magellan: Toward building entity matching management systems
P. Konda, S. Das, P. S. G. C., A. Doan, A. Ardalan, J. R. Ballard, H. Li, F. Panahi, H. Zhang, J. F. Naughton, S. Prasad, G. Krishnan, R. Deep, and V. Raghavendra · 2016
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Complex embeddings for simple link prediction
T. Trouillon, J. Welbl, S. Riedel, É. Gaussier, and G. Bouchard · 2016
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Inductive representation learning on large graphs
W. L. Hamilton, Z. Ying, and J. Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
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Unrolled generative adversarial networks
L. Metz, B. Poole, D. Pfau, and J. Sohl-Dickstein · 2017
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Generating concise entity matching rules
R. Singh, V. V. Meduri, A. K. Elmagarmid, S. Madden, P. Papotti, J. Quiané-Ruiz, A. Solar-Lezama, and N. Tang · 2017
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Synthesizing entity matching rules by examples
R. Singh, V. V. Meduri, A. K. Elmagarmid, S. Madden, P. Papotti, J. Quiané-Ruiz, A. Solar-Lezama, and N. Tang · 2017
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Stochastic training of graph convolutional networks with variance reduction
J. Chen, J. Zhu, and L. Song · 2018
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Data integration and machine learning: A natural synergy
X. L. Dong and T. Rekatsinas · 2018
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Distributed representations of tuples for entity resolution
M. Ebraheem, S. Thirumuruganathan, S. R. Joty, M. Ouzzani, and N. Tang · 2018
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Deep text classification can be fooled
B. Liang, H. Li, M. Su, P. Bian, X. Li, and W. Shi · 2018
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Deep learning for entity matching: A design space exploration
S. Mudgal, H. Li, T. Rekatsinas, A. Doan, Y. Park, G. Krishnan, R. Deep, E. Arcaute, and V. Raghavendra · 2018
Cited alongside, same era.
Deep learning for entity matching: A design space exploration
S. Mudgal, H. Li, T. Rekatsinas, A. Doan, Y. Park, G. Krishnan, R. Deep, E. Arcaute, and V. Raghavendra · 2018
Xlnet: Generalized autoregressive pretraining for language understanding
Z. Yang, Z. Dai, Y. Yang, J. G. Carbonell, R. Salakhutdinov, and Q. V. Le · 2019
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Auto-em: End-to-end fuzzy entity-matching using pre-trained deep models and transfer learning
C. Zhao and Y. He · 2019
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USAD: unsupervised anomaly detection on multivariate time series
J. Audibert, P. Michiardi, F. Guyard, S. Marti, and M. A. Zuluaga · 2020
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Entity matching with transformer architectures - A step forward in data integration
U. Brunner and K. Stockinger · 2020
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Creating embeddings of heterogeneous relational datasets for data integration tasks
R. Cappuzzo, P. Papotti, and S. Thirumuruganathan · 2020
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Improving the efficiency and effectiveness for bert-based entity resolution
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Bootstrapping entity alignment with knowledge graph embedding
Z. Sun, W. Hu, Q. Zhang, and Y. Qu · 2018
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Graph attention networks
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio · 2018
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Deep autoencoding gaussian mixture model for unsupervised anomaly detection
B. Zong, Q. Song, M. R. Min, W. Cheng, C. Lumezanu, D. Cho, and H. Chen · 2018
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Adversarial attacks on node embeddings via graph poisoning
A. Bojchevski and S. Günnemann · 2019
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BERT: pre-training of deep bidirectional transformers for language understanding
J. Devlin, M. Chang, K. Lee, and K. Toutanova · 2019
Cited alongside, same era.
End-to-end multi-perspective matching for entity resolution
C. Fu, X. Han, L. Sun, B. Chen, W. Zhang, S. Wu, and H. Kong · 2019
Cited alongside, same era.
B. Li, Y. Miao, Y. Wang, Y. Sun, and W. Wang · 2020
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Grapher: Token-centric entity resolution with graph convolutional neural networks
B. Li, W. Wang, Y. Sun, L. Zhang, M. A. Ali, and Y. Wang · 2020
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Deep entity matching with pre-trained language models
Y. Li, J. Li, Y. Suhara, A. Doan, and W. Tan · 2020
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Exploring and evaluating attributes, values, and structures for entity alignment
Z. Liu, Y. Cao, L. Pan, J. Li, and T. Chua · 2020
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Relational reflection entity alignment
X. Mao, W. Wang, H. Xu, Y. Wu, and M. Lan · 2020
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Ergan: Generative adversarial networks for entity resolution
J. Shao, Q. Wang, A. Wijesinghe, and E. Rahm · 2020
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A benchmarking study of embedding-based entity alignment for knowledge graphs
Z. Sun, Q. Zhang, W. Hu, C. Wang, M. Chen, F. Akrami, and C. Li · 2020
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Zeroer: Entity resolution using zero labeled examples
R. Wu, S. Chaba, S. Sawlani, X. Chu, and S. Thirumuruganathan · 2020
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Degree-aware alignment for entities in tail
W. Zeng, X. Zhao, W. Wang, J. Tang, and Z. Tan · 2020
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Unsupervised entity resolution with blocking and graph algorithms
D. Zhang, D. Li, L. Guo, and K.-L. Tan · 2020
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Multi-context attention for entity matching
D. Zhang, Y. Nie, S. Wu, Y. Shen, and K. Tan · 2020
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Distdgl: Distributed graph neural network training for billion-scale graphs
D. Zheng, C. Ma, M. Wang, J. Zhou, Q. Su, X. Song, Q. Gan, Z. Zhang, and G. Karypis · 2020
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