Entity resolution with evolving rules

Entity resolution (ER) identifies database records that refer to the same real world entity. In practice, ER is not a one-time process, but is constantly improved as the data, schema and application are better understood. We address the problem of keeping the ER re-sult up-to-date when the ER logic “evolves ” frequently. A naı̈ve approach that re-runs ER from scratch may not be tolerable for re-solving large datasets. This paper investigates when and how we can instead exploit previous “materialized ” ER results to save re-dundant work with evolved logic. We introduce algorithm proper-ties that facilitate evolution, and we propose efficient rule evolution techniques for two clustering ER models: match-based clustering and distance-based clustering. Using real data sets, we illustrate the cost of materializations and the potential gains over the naı̈ve approach. 1