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Entity Matching in the Wild: A Consistent and Versatile Framework to Unify Data in Industrial Applications

Summary: Fusion: an industrial entity-matching framework using ordinal-regression scoring to unify records at multiple confidence levels. A clustering approach tolerates missing data via transitive linking and a persistent-ID to sustain stable IDs for evolving data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5903
Venue
SIGMOD
Year
2020
Pagerank
4.4079449e-05
Overall Rank
9,020 | 37.25%
DOI
10.1145/3318464.3386143

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
3,711 Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale 2022 SIGMOD 6.823609e-05
11,223 Splitting Tuples of Mismatched Entities 2023 SIGMOD 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

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
398 Big Data Integration 2013 VLDB 0.00024372588
667 Incremental Knowledge Base Construction Using DeepDive 2015 VLDB 0.00018440557
3,177 Evaluating Entity Resolution Results 2010 VLDB 7.4367331e-05
4,383 Incremental Record Linkage 2014 VLDB 6.2383094e-05
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