My colleagues and I have just published on arXiv a simple but highly effective Entity Resolution algorithm that can scale to billions of records and handle significant data quality issues. The paper is titled Scalable Entity Resolution Using Probabilistic Signatures on Parallel Databases and it is an extension of our previous paper on linking millions of addresses at scale.
Here is the abstract of the paper.
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.
Mental Models 4 Life 