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Large-Scale Collective Entity Matching

Summary: Proposes a principled, neighborhood-based framework to scale any generic Entity Matching (EM) algorithm by running multiple EM instances on small data neighborhoods and exchanging messages to converge on a global solution. It provides formal properties and empirical validation for scalable EM on large real-world datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10250
Venue
VLDB
Year
2011
Pagerank
6.8853274e-05
Overall Rank
3,645 | 74.65%
DOI
-

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Showing 4 of 4 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
229 Reference Reconciliation in Complex Information Spaces 2005 SIGMOD 0.00032242633
280 Eliminating Fuzzy Duplicates in Data Warehouses 2002 VLDB 0.00029113044
684 Towards a Robust Query Optimizer: A Principled and Practical Approach 2005 SIGMOD 0.00018179769
1,410 Entity Resolution with Iterative Blocking 2009 SIGMOD 0.00012127555
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