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English Information

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Authors
# Name
1 Luma Pires(lumacicilia@usp.br)
2 Jorge Junior(jorgerady@usp.br)

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Reference
# Reference
1 BANSAL, N.; BLUM, A.; CHAWLA, S. Correlation clustering. In: IEEE SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE, 43., 2002. Proceedings [...]. [S.l.: s.n.], 2002. p. 238–247.
2 BINETTE, O.; STEORTS, R. (Almost) All of Entity Resolution. Science Advances, v. 8, 2022.
3 CAO, H. et al. Graph Deep Active Learning Framework for Data Deduplication. Big Data Mining and Analytics, v. 7, n. 3, p. 753–764, 2024.
4 CARVALHO, G. H. de et al. Ice-ID: A Novel Historical Census Data Benchmark Comparing NARS against LLMs & A ML Ensemble on Longitudinal Identity Resolution. arXiv preprint, 2025.
5 CHRISTEN, V. et al. Graph Metrics-driven Record Cluster Repair meets LLM-based active learning. Journal of Data and Information Quality, v. 17, n. 2, 2025.
6 DEMAINE, E. D. et al. Correlation Clustering in general weighted graphs. Theoretical Computer Science, v. 361, n. 2, p. 172–187, 2006.
7 DRAISBACH, U.; CHRISTEN, P.; NAUMANN, F. Transforming Pairwise Duplicates to Entity Clusters for High-quality Duplicate Detection. Journal of Data and Information Quality, v. 12, n. 1, 2019.
8 FU, J. et al. In-context Clustering-based Entity Resolution with Large Language Models: A Design Space Exploration. Proceedings of the ACM on Management of Data, v. 3, n. 4, 2025.
9 HUANG, K. et al. ThriftLLM: On Cost-Effective Selection of Large Language Models for Classification Queries. Proceedings of the VLDB Endowment, v. 18, n. 11, p. 4410–4423, 2025.
10 LI, H. et al. On leveraging Large Language Models for Enhancing Entity Resolution. arXiv preprint, 2024.
11 LI, Y. et al. Deep Entity Matching with Pre-Trained Language Models. Proceedings of the VLDB Endowment, v. 14, n. 1, p. 50–60, 2020.
12 NIKOLETOS, K.; IOANNOU, E.; PAPADAKIS, G. The Five Generations of Entity Resolution on Web Data. In: STEFANIDIS, K. et al. (ed.). Web Engineering. Cham: Springer, 2024. p. 469–473.
13 OKAYAMA, K.; ITO, H.; MORISHIMA, A. Learning from Unknown-Unknowns: Inconsistency-Driven Sampling for Improving LLM Entity Matching. Information Research, 2026.
14 PARDO, F. de M. et al. GraLMatch: Matching Groups of Entities with Graphs and Language Models. In: SIMITSIS, A. et al. (ed.). Proceedings of the 28th International Conference on Extending Database Technology (EDBT 2025). Barcelona: OpenProceedings.org, 2025. p. 1–12.
15 PARDO, F. de M. et al. TransClean: Finding False Positives In Multi-Source Entity Matching Under Real-World Conditions Via Transitive Consistency. IEEE Access, v. 13, p. 195856–195870, 2025.
16 PEETERS, R.; BIZER, C. Dual-Objective Fine-Tuning of BERT for Entity Matching. Proceedings of the VLDB Endowment, v. 14, n. 10, p. 1913–1921, 2021.
17 PEETERS, R.; BIZER, C. Using ChatGPT for Entity Matching. In: EUROPEAN CONFERENCE ON ADVANCES IN DATABASES AND INFORMATION SYSTEMS. [S.l.]: Springer, 2023. p. 221–230.
18 SAEEDI, A.; PEUKERT, E.; RAHM, E. Comparative Evaluation of Distributed Clustering Schemes for Multi-Source Entity Resolution. In: KIRIKOVA, M.; NØRVÅG, K.; PAPADOPOULOS, G. A. (ed.). Advances in Databases and Information Systems. Cham: Springer, 2017. p. 278–293.
19 WANG, T. et al. Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching. In: Proceedings of the 31st International Conference on Computational Linguistics. 2025. p. 96–109.
20 ZEAKIS, A. et al. AvengER: Ensembling and Fine-Tuning LLMs for SELECT prompts in Entity Resolution. In: Extended Semantic Web Conference. 2025.