Fairness-Oriented Entity Resolution Tool for Streaming Data

• Tiago Brasileiro Araújo Instituto Federal de Educação, Ciência e Tecnologia da Paraíba (IFPB) / Tampere University http://orcid.org/0000-0001-6339-9117
• Vasilis Efthymiou Harokopio University of Athens / FORTH-ICS
• Kostas Stefanidis Tampere University https://orcid.org/0000-0003-1317-8062
• Rafael de Souza Guerra Universidade Federal de Campina Grande (UFCG)

DOI: https://doi.org/10.5753/sbbd_estendido.2024.242809

Resumo

Entity Resolution (ER) plays a crucial role, facilitating the integration of knowledge bases and identifying similarities among entities from different sources. In this work, we address the following challenges: streaming data, incremental processing, and fairness. There is a lack of studies involving fairness and ER, which is related to the absence of discrimination or bias. Considering this context, this work presents TREATS, a fairness-aware ER tool able to deal with streaming and incremental data, which goes beyond matching based on the similarity scores and also applies to target fairness constraints. Overall, our contributions aim to advance the field of ER by offering a matching tool that considers both technical challenges and ethical considerations.

Palavras-chave: Entity Resolution, Machine Learning, Fairness, Data Quality

Referências

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