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English Information

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Authors
# Name
1 Tiago Araújo(tiago.brasileiro@ifpb.edu.br)
2 Vasilis Efthymiou(vefthym@hua.gr)
3 Kostas Stefanidis(konstantinos.stefanidis@tuni.fi)
4 Rafael Guerra(rafael.guerra@ccc.ufcg.edu.br)

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Reference
# Reference
1 Araújo, T. B., Stefanidis, K., Pires, C. E. S., Nummenmaa, J., & da Nóbrega, T. P. (2022). Incremental Entity Blocking over Heterogeneous Streaming Data. Information, 13(12), 568.
2 Chen, R., Shen, Y., & Zhang, D. (2021, April). GNEM: a generic one-to-set neural entity matching framework. In Proceedings of the Web Conference 2021 (pp. 1686-1694).
3 Christophides, V., Efthymiou, V., Palpanas, T., Papadakis, G., & Stefanidis, K. (2020). An overview of end-to-end entity resolution for big data. ACM Computing Surveys (CSUR), 53(6), 1-42.
4 Christophides, V., Efthymiou, V., & Stefanidis, K. (2015). Entity resolution in the web of data (Vol. 5, No. 3, pp. 1-122). San Rafael: Morgan & Claypool.
5 Efthymiou, V., Stefanidis, K., Pitoura, E., & Christophides, V. (2021, October). FairER: Entity resolution with fairness constraints. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (pp. 3004-3008).
6 Li, Y., Li, J., Suhara, Y., Doan, A., and Tan, W.-C. (2020). Deep entity matching with pre-trained language models. Proceedings of the VLDB Endowment, 14(1):50–60.
7 Pitoura, E., Stefanidis, K., & Koutrika, G. (2022). Fairness in rankings and recommendations: an overview. The VLDB Journal, 1-28.
8 Shahbazi, N., Danevski, N., Nargesian, F., Asudeh, A., and Srivastava, D. (2023). Through the fairness lens: Experimental analysis and evaluation of entity matching. Proceedings of the VLDB Endowment, 16(11):3279–3292.