Severe Name Homonymy: A Learning to Rank Approach for Civil Registry Entity Linking - Dutra, Marco T.; Costa, Lucas G. L.; Martins, Felipe D. S.; Freitas, Gabriela M. M.; Castro, Leonardo C. C.; Vieira, Pedro G. B.; Brandão, Michele A.; Pappa, Gisele L. (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais do XLI Simpósio Brasileiro de Bancos de DadosDO - 10.5753/sbbd.2026.249274, 2026)
This study addresses the challenge of transforming string-based kinship references into explicit entity relationships within large-scale civil registries. In scenarios where a record provides only the name of a relative (e.g., a mother’s name), identifying the correct unique identifier among numerous homonymous candidates constitutes a complex Known-Item Search problem. We propose a supervised Learning to Rank framework designed to disambiguate and rank these candidates by integrating kinship-based relational features, conditional probability signals, demographic indicators, and BERT-based semantic similarity. The approach was evaluated on a large-scale private dataset from Brazil and four public datasets: IPUMS (covering USA, Norway, and Iceland) and Wikidata. Experimental results demonstrate that our method provides a robust solution for identity resolution under high name ambiguity, achieving a Recall@5 above 0.90 across all evaluated datasets and enabling the construction of accurate entity links in civil databases.