  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 allows it to be deployed with relative ease in large
industrial applications.
