Entity Resolution with Evolving Rules

Abstract

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 result up-to-date when the ER logic "evolves" frequently. A naive approach that re-runs ER from scratch may not be tolerable for resolving large datasets. This paper investigates when and how we can instead exploit previous "materialized" ER results to save redundant work with evolved logic. We introduce algorithm properties that facilitate evolution, and we propose efficient rule evolution techniques for three ER models: join, match-based clustering, and distance-based clustering. Using real data sets, we illustrate the cost of materializations and the potential gains over the naive approach.

Available Versions of this Item

• Entity Resolution with Evolving Rules. (deposited 08 Mar 2010 08:24) [Currently Displayed]
  • Entity Resolution with Evolving Rules. (deposited 02 Jul 2010 10:43)