Entity Resolution with Evolving Rules

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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 na\"\i ve 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 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 na\"\i ve approach.

Available Versions of this Item

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