@article {14394,
	title = {Relational clustering for entity resolution queries},
	journal = {ICML 2006 Workshop on Statistical Relational Learning (SRL)},
	year = {2006},
	month = {2006///},
	abstract = {The goal of entity resolution is to recon-cile database references corresponding to the
same real-world entities. Given the abun-
dance of publicly available databases where
entities are not resolved, we motivate the
problem of quickly processing queries that
require resolved entities from such {\textquoteleft}unclean{\textquoteright}
databases. We first propose a cut-based rela-
tional clustering formulation for collective en-
tity resolution. We then show how it can be
performed on-the-fly by adaptively extract-
ing 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 ap-
proach while preserving the gains of collective
resolution.
},
	author = {Bhattacharya,I. and Licamele,L. and Getoor, Lise}
}
