  Some of the greatest advances in web search have come from leveraging
socio-economic properties of online user behavior. Past advances include
PageRank, anchor text, hubs-authorities, and TF-IDF. In this paper, we
investigate another socio-economic property that, to our knowledge, has not yet
been exploited: sites that create lists of entities, such as IMDB and Netflix,
have an incentive to avoid gratuitous duplicates. We leverage this property to
resolve entities across the different web sites, and find that we can obtain
substantial improvements in resolution accuracy. This improvement in accuracy
also translates into robustness, which often reduces the amount of training
data that must be labeled for comparing entities across many sites.
Furthermore, the technique provides robustness when resolving sites that have
some duplicates, even without first removing these duplicates. We present
algorithms with very strong precision and recall, and show that max weight
matching, while appearing to be a natural choice turns out to have poor
performance in some situations. The presented techniques are now being used in
the back-end entity resolution system at a major Internet search engine.
