Entity Matching: How Similar Is Similar
Summary: Addresses 'how similar is similar' in entity matching by pruning the space of similarity functions and thresholds. Introduces optimization to remove redundancy and efficient algorithms to pick the best functions, with experiments on real and synthetic data showing improved accuracy over baselines. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jiannan Wang
- 2. Guoliang Li
- 3. Jeffrey Xu Yu
- 4. Jianhua Feng
Incoming Citations (Sorted by Pagerank)
Showing 16 of 16 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 67 | The Merge/Purge Problem for Large Databases | 1995 | SIGMOD | 0.00061348205 |
| 199 | Declarative Data Cleaning: Language, Model, and Algorithms | 2001 | VLDB | 0.00035041015 |
| 266 | Efficient Exact Set-Similarity Joins | 2006 | VLDB | 0.00029718727 |
| 322 | Record Linkage: Similarity Measures and Algorithms | 2006 | SIGMOD | 0.00027518768 |
| 702 | Reasoning about Record Matching Rules | 2009 | VLDB | 0.00017918203 |
| 1,533 | Example-driven Design of Efficient Record Matching Queries | 2007 | VLDB | 0.00011471971 |
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