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- W4313703119 abstract "Abstract Entity resolution (record linkage or deduplication) is the process of identifying and linking duplicate records in databases. In this paper, we propose a Bayesian graphical approach for entity resolution that links records to latent entities, where the prior representation on the linkage structure is exchangeable. First, we adopt a flexible and tractable set of priors for the linkage structure, which corresponds to a special class of random partition models. Second, we propose a more realistic distortion model for categorical/discrete record attributes, which corrects a logical inconsistency with the standard hit-miss model. Third, we incorporate hyperpriors to improve flexibility. Fourth, we employ a partially collapsed Gibbs sampler for inferential speedups. Using a selection of private and nonprivate data sets, we investigate the impact of our modeling contributions and compare our model with two alternative Bayesian models. In addition, we conduct a simulation study for household survey data, where we vary distortion, duplication rates and data set size. We find that our model performs more consistently than the alternatives across a variety of scenarios and typically achieves the highest entity resolution accuracy (F1 score). Open source software is available for our proposed methodology, and we provide a discussion regarding our work and future directions." @default.
- W4313703119 created "2023-01-08" @default.
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- W4313703119 date "2023-01-05" @default.
- W4313703119 modified "2023-10-18" @default.
- W4313703119 title "Bayesian Graphical Entity Resolution using Exchangeable Random Partition Priors" @default.
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- W4313703119 doi "https://doi.org/10.1093/jssam/smac030" @default.
- W4313703119 hasPublicationYear "2023" @default.
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