Pre-trained Embeddings for Entity Resolution: An Experimental Analysis
Summary: Thorough empirical study of 12 pre-trained embeddings (fastText, BERT variants) on 17 ER benchmarks, measuring vectorization cost, blocking scalability vs a SOTA deep blocker, and supervised/unsupervised matching performance. Provides actionable insights on encoding vs accuracy trade-offs. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,894 | TableDC: Deep Clustering for Tabular Data | 2025 | SIGMOD | 4.8925595e-05 |
| 8,852 | Watchog: A Light-weight Contrastive Learning based Framework for Column Annotation | 2023 | SIGMOD | 4.4356508e-05 |
| 9,855 | Progressive Entity Matching: A Design Space Exploration | 2025 | SIGMOD | 4.269353e-05 |
| 9,984 | Towards Scalable Visual Data Wrangling via Direct Manipulation | 2026 | CIDR | 4.1945683e-05 |
| 11,063 | Searching Data Lakes for Nested and Joined Data | 2024 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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