Scalable entity resolution using probabilistic signatures on parallel databases
Date
2018
Authors
Zhang, Yuhang
Ng, Kee Siong
Churchill, Tania
Christen, Peter
Journal Title
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Volume Title
Publisher
Association for Computing Machinery (ACM)
Abstract
Accurate 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.
Description
Keywords
Large-scale entity resolution, connected components, probabilistic signature, in-database analytics
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Source
International Conference on Information and Knowledge Management, Proceedings
Type
Conference paper
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Restricted until
2099-12-31