The goal of entity resolution is to reconcile database references corresponding to the same real-world entities. Given the abundance of publicly available databases where entities are not resolved, we motivate the problem of quickly processing queries that require resolved entities from such `unclean' databases. We first propose a cut-based relational clustering formulation for collective entity resolution. We then show how it can be performed on-the-fly by adaptively extracting and resolving those database references that are the most helpful for resolving the query. We validate our approach on two large real-world publication databases, where we show the usefulness of collective resolution and at the same time demonstrate the need for adaptive strategies for query processing. We then show how the same queries can be answered in real time using our adaptive approach while preserving the gains of collective resolution.

@InProceedings{bhattacharya:icml06-wkshp,
  author       = "Bhattacharya, Indrajit and Licamele, Louis and Getoor, Lise",
  title        = "Relational Clustering for Entity Resolution Queries",
  booktitle    = "ICML Workshop on Statistical Relational Learning (SRL)",
  year         = "2006",
}