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English Information

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Authors
# Name
1 Rodolfo Bolconte(rodolfo.donato@ifpb.edu.br)
2 Tiago Brasileiro Araújo(tiago.brasileiro@ifpb.edu.br)
3 Carlos Eduardo Pires(cesp@dsc.ufcg.edu.br)
4 Demetrio Mestre(demetrio@computacao.ufcg.edu.br)

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Reference
# Reference
1 Araujo, T. B., Efthymiou, V., Christophides, V., Pitoura, E., and Stefanidis, K. (2025a). Treats: Fairness-aware entity resolution over streaming data. Information Systems, 129:102506.
2 Araujo, T. B., Efthymiou, V., and Stefanidis, K. (2025b). Fairness and explanations in entity resolution: An overview. IEEE Access.
3 Araujo, T. B., Stefanidis, K., Santos Pires, C. E., Nummenmaa, J., and Da Nóbrega, T. P. (2020). Schema-agnostic blocking for streaming data. In Proceedings of the 35th Annual ACM Symposium on Applied Computing, pages 412–419.
4 Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D. (2020). Language models are few-shot learners.
5 Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., Ye, W., Zhang, Y., Chang, Y., Yu, P. S., Yang, Q., and Xie, X. (2024). A survey on evaluation of large language models. ACM Trans. Intell. Syst. Technol., 15(3).
6 Christophides, V., Efthymiou, V., Palpanas, T., Papadakis, G., and Stefanidis, K. (2019). End-to-end entity resolution for big data: A survey. arXiv preprint arXiv:1905.06397.
7 Donato, R. B. and Araujo, T. B. (2025). Entity matching com large language models: estudo comparativo com abordagem de entity blocking. In Anais do XL Simpósio Brasileiro de Bancos de Dados, pages 956–962, Porto Alegre, RS, Brasil. SBC.
8 Ebraheem, M., Thirumuruganathan, S., Joty, S., Ouzzani, M., and Tang, N. (2018). Distributed representations of tuples for entity resolution. Proc. VLDB Endow., 11(11):1454–1467.
9 Li, Y., Li, J., Suhara, Y., Doan, A., and Tan, W. (2020). Deep entity matching with pre-trained language models. CoRR, abs/2004.00584.
10 Maciejewski, J., Nikoletos, K., Papadakis, G., and Velegrakis, Y. (2025). Progressive entity matching: A design space exploration. Proc. ACM Manag. Data, 3(1).
11 Mestre, D. G., Pires, C. E. S., Nascimento, D. C., de Queiroz, A. R. M., Santos, V. B., and Araujo, T. B. (2017). An efficient spark-based adaptive windowing for entity matching. Journal of Systems and Software, 128:1–10.
12 Papadakis, G., Efthymiou, V., Thanos, E., Hassanzadeh, O., and Christen, P. (2023). An analysis of one-to-one matching algorithms for entity resolution.
13 Peeters, R., Der, R. C., and Bizer, C. (2023). Wdc products: A multi-dimensional entity matching benchmark.
14 Peeters, R., Steiner, A., and Bizer, C. (2024). Entity matching using large language models.
15 Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., Chi, E. H., Hashimoto, T., Vinyals, O., Liang, P., Dean, J., and Fedus, W. (2022). Emergent abilities of large language models.
16 Xia, Y., Chen, J., Li, X., and Gao, J. (2024). Aprompt4em: Augmented prompt tuning for generalized entity matching.
17 Zeakis, A., Papadakis, G., Skoutas, D., and Koubarakis, M. (2025). An in-depth analysis of pre-trained embeddings for entity resolution: An in-depth analysis of pre-trained embeddings for entity resolution: A. zeakis et al. VLDB Journal International Journal on Very Large Data Bases, 34(1).
18 Zhang, Z., Groth, P., Calixto, I., and Schelter, S. (2025). A deep dive into cross-dataset entity matching with large and small language models. In Advances in Database Technology - EDBT, number 3 in Advances in Database Technology - EDBT, pages 922–934. OpenProceedings.org.
19 Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., Du, Y., Yang, C., Chen, Y., Chen, Z., Jiang, J., Ren, R., Li, Y., Tang, X., Liu, Z., Liu, P., Nie, J.-Y., and Wen, J.-R. (2025). A survey of large language models.