Zhang, YuhangNg, Kee SiongChurchill, TaniaChristen, PeterPaton, NCandan, SWan, HAllan, JAgrawal, RLabrinidis, A2024-02-13October 22978-145036014-2http://hdl.handle.net/1885/313562Accurate and efficient entity resolution is an open challenge of particular relevance to intelligence organisations that collect large datasets from disparate sources with differing levels of quality and standard. Starting from a first-principles formulation of entity resolution, this paper presents a novel entity resolution algorithm that introduces a data-driven blocking and record linkage technique based on the probabilistic identification of entity signatures in data. The scalability and accuracy of the proposed algorithm are evaluated using benchmark datasets and shown to achieve state-of-the-art results. The proposed algorithm can be implemented simply on modern parallel databases, which we have done in the financial intelligence domain with tens of Terabytes of noisy data.application/pdfen-AU© 2018 Copyright held by the owner/author(s).Publication rights licensed to ACMLarge-scale entity resolutionconnected componentsprobabilistic signaturein-database analyticsScalable entity resolution using probabilistic signatures on parallel databases201810.1145/3269206.32720162022-10-02