Large-Scale Deduplication with Constraints Using Dedupalog

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SIGMa: simple greedy matching for aligning large knowledge bases (2012, methodology): “Some scalable solutions for collective entity resolution were proposed recently [1, 25], though they did not implement a 1–1 matching constraint, and their implementation can be a complex software engineering endeavor in contrast to the simplicity of our approach.”
Trends in Cleaning Relational Data: Consistency and Deduplication (2015, background): “Deduplog [6] proposes a declarative way of specifying the additional hard constraint, such as “two authors of the same paper have to be different”, as well as soft constraint, such as “two authors are more likely to be duplicates if they have a common coauthor”, and aims at minimizing the number of ”
Frameworks for entity matching: A comparison (2010, background): “Many approaches, e.g. [33] [37] [2], require a human expert to specify such match rules declaratively.”
Joint Entity Resolution (2012, methodology): “Among the existing works on joint ER [1], [2], [3], [4], [5], [6], few have focused on scalability, which is crucial in resolving large data (e.”
PARIS: Probabilistic Alignment of Relations, Instances, and Schema (2011, methodology): “There have been many works on entity resolution, i.e., on what is traditionally known as the “A-Box” [1, 4, 12, 17, 18,25,27,28,31].”