User facing topical web applications such as events or shopping sites rely on large collections of data records about real world entities that are updated at varying latencies ranging from days to seconds. For example, event venue details are changed relatively infrequently whereas ticket pricing and availability for an event is often updated in near-realtime. Users regard these sites as high quality if they seldom show duplicates, the URLs are stable, and their content is fresh, so it is important to resolve duplicate entity records with high quality and low latencies. High quality entity resolution typically evaluates the entire record corpus for similar record clusters at the cost of latency, while low latency resolution examines the least possible entities to keep time to a minimum, even at the cost of quality. In this paper we show how to keep low latency while achieving high quality, combining the best of both approaches: given an entity to be resolved, our incremental Fastpath system, in a matter of milliseconds, makes approximately the same decisions that the underlying batch system would have made. Our experiments show that the Fastpath system makes matching decisions for previously unseen entities with 90% precision and 98% recall relative to batch decisions, with latencies under 20ms on commodity hardware.
['Michael J. Welch', 'Aamod Sane', 'Chris Drome']
Fast and accurate incremental entity resolution relative to an entity knowledge base