Scaling Record Linkage to Non-uniform Distributed Class Sizes

Record linkage is a central task when information from different sources is integrated. Record linkage models use so-called blockers for reducing the search space by discarding obviously different record pairs. In practice, important problems have Zipf distributed class sizes with some large classes where blocking is not applicable any more. Therefore we propose two novel meta algorithms for scaling arbitrary record linkage models to such data sets. The first one parallelizes problems by creating overlapping subproblems and the second one reduces the search space for large classes effectively. Our evaluation shows that both scaling techniques are effective and are able to scale state-of-the-art models to challenging datasets.