D-blink: Distributed End-to-End Bayesian Entity Resolution. Issue 2 (16th February 2021)

• Record Type: Journal Article
• Authors: Marchant, Neil G.; Kaplan, Andee; Elazar, Daniel N.; Rubinstein, Benjamin I. P.; Steorts, Rebecca C.
• Is Part Of: Journal of computational and graphical statistics. Volume 30:Issue 2(2021)
• Page Start: 406
• Page End: 421
• Publication Date: 2021-02-16
• Subjects: Auxiliary variable -- Distributed computing -- Markov chain Monte Carlo -- Partially collapsed Gibbs sampling -- Record linkage; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2020.1825451
• Languages: English
• ISSNs: 1061-8600

Abstract

Abstract: Entity resolution (ER; also known as record linkage or de-duplication) is the process of merging noisy databases, often in the absence of unique identifiers. A major advancement in ER methodology has been the application of Bayesian generative models, which provide a natural framework for inferring latent entities with rigorous quantification of uncertainty. Despite these advantages, existing models are severely limited in practice, as standard inference algorithms scale quadratically in the number of records. While scaling can be managed by fitting the model on separate blocks of the data, such a naïve approach may induce significant error in the posterior. In this article, we propose a principled model for scalable Bayesian ER, called "distributed Bayesian linkage" or d-blink, which jointly performs blocking and ER without compromising posterior correctness. Our approach relies on several key ideas, including: (i) an auxiliary variable representation that induces a partition of the entities and records into blocks; (ii) a method for constructing well-balanced blocks based on k-d trees; (iii) a distributed partially collapsed Gibbs sampler with improved mixing; and (iv) fast algorithms for performing Gibbs updates. Empirical studies on six datasets—including a case study on the 2010 Decennial Census—demonstrate the scalability and effectiveness of our approach. Supplementary materials for this article are available online.