Fetching the paper…
Reading the bibliography…
Entity resolution, the task of identifying and merging records that refer to the same real-world entity, is crucial in sectors like e-commerce, healthcare, and law enforcement.
A theory for record linkage
Ivan P Fellegi and Alan B Sunter. 1969 · 1969
Earlier work this paper cites.
Advances in record-linkage methodology as applied to matching the 1985 census of Tampa, Florida
Matthew A Jaro. 1989 · 1989
Earlier work this paper cites.
The budgeted maximum coverage problem
Samir Khuller, Anna Moss, and Joseph Seffi Naor. 1999 · 1999
Earlier work this paper cites.
Probabilistic record linkage and a method to calculate the positive predictive value
Tony Blakely and Clare Salmond. 2002 · 2002
Earlier work this paper cites.
Duplicate record detection: A survey
Ahmed K Elmagarmid, Panagiotis G Ipeirotis, and Vassilios S Verykios. 2006 · 2006
Earlier work this paper cites.
Collective entity resolution in relational data
Indrajit Bhattacharya and Lise Getoor. 2007 · 2007
Earlier work this paper cites.
Quality and complexity measures for data linkage and deduplication
Peter Christen and Karl Goiser. 2007 · 2007
Earlier work this paper cites.
Creating probabilistic databases from duplicated data
Oktie Hassanzadeh and Renée J Miller. 2009 · 2009
Earlier work this paper cites.
Levenshtein Distance: Information Theory, Computer Science, String (Computer Science), String Metric, Damerau?Levenshtein Distance, Spell Checker, Hamming Distance
Frederic P. Miller, Agnes F. Vandome, and John McBrewster. 2009 · 2009
Earlier work this paper cites.
Entity resolution for big data. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining . 1527–1527
Lise Getoor and Ashwin Machanavajjhala. 2013 · 2013
Cited alongside, same era.
Matching and record linkage
William E Winkler. 2014 · 2014
Cited alongside, same era.
Notes on greedy algorithms for submodular maximization
Thibaut Horel. 2015 · 2015
Cited alongside, same era.
Muhammad Ebraheem, Saravanan Thirumuruganathan, Shafiq Joty, Mourad Ouzzani, and Nan Tang. 2017 · 2017
Cited alongside, same era.
End-to-end entity resolution for big data: A survey
Vassilis Christophides, Vasilis Efthymiou, Themis Palpanas, George Papadakis, and Kostas Stefanidis. 2019 · 2019
Cited alongside, same era.
Generic entity resolution models. In NeurIPS 2022 First Table Representation Workshop
Jiawei Tang, Yifei Zuo, Lei Cao, and Samuel Madden. 2022 · 2022
Later among the works it cites.
Recent advances in natural language processing via large pre-trained language models: A survey
Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heintz, and Dan Roth. 2023 · 2023
Later among the works it cites.
Entity matching using large language models
Ralph Peeters and Christian Bizer. 2023 · 2023
Later among the works it cites.
Pre-Trained Embeddings for Entity Resolution: An Experimental Analysis
Alexandros Zeakis, George Papadakis, Dimitrios Skoutas, and Manolis Koubarakis. 2023 · 2023
Later among the works it cites.
Large language models as data preprocessors
Haochen Zhang, Yuyang Dong, Chuan Xiao, and Masafumi Oyamada. 2023 · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Python Record Linkage Toolkit: A toolkit for record linkage and duplicate detection in Python
J De Bruin. 2019 · 2019
Cited alongside, same era.
Deep entity matching with pre-trained language models
Yuliang Li, Jinfeng Li, Yoshihiko Suhara, AnHai Doan, and Wang-Chiew Tan. 2020a · 2020
Cited alongside, same era.
Deep entity matching with pre-trained language models
Yuliang Li, Jinfeng Li, Yoshihiko Suhara, AnHai Doan, and Wang-Chiew Tan. 2020b · 2020
Cited alongside, same era.
Can foundation models wrangle your data?
Avanika Narayan, Ines Chami, Laurel Orr, Simran Arora, and Christopher Ré. 2022 · 2022
Cited alongside, same era.
Later among the works it cites.
A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al · 2023
Later among the works it cites.
Cost-effective in-context learning for entity resolution: A design space exploration. In 2024 IEEE 40th International Conference on Data Engineering (ICDE) . IEEE, 3696–3709
Meihao Fan, Xiaoyue Han, Ju Fan, Chengliang Chai, Nan Tang, Guoliang Li, and Xiaoyong Du. 2024 · 2024
Closest in time.
BoostER: Leveraging Large Language Models for Enhancing Entity Resolution. In Companion Proceedings of the ACM on Web Conference 2024 . 1043–1046
Huahang Li, Shuangyin Li, Fei Hao, Chen Jason Zhang, Yuanfeng Song, and Lei Chen. 2024 · 2024
Closest in time.