Analyzing How BERT Performs Entity Matching
Summary: Multi-facet analysis of BERT-based EM: fine-tuning mainly reshapes the last layers, with different effects on matching vs non-matching tokens. BERT also leverages the pairwise-structured descriptions, while pairwise token similarity is not the core knowledge exploited. (summarized by gpt-5-nano 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 |
|---|---|---|---|---|
| 3,397 | Automatic Data Repair: Are We Ready to Deploy? | 2024 | VLDB | 7.1386386e-05 |
| 6,558 | Pre-trained Embeddings for Entity Resolution: An Experimental Analysis | 2023 | VLDB | 5.0060112e-05 |
| 8,910 | Deep Active Alignment of Knowledge Graph Entities and Schemata | 2023 | SIGMOD | 4.4229886e-05 |
| 10,625 | Deduplicated Sampling On-Demand | 2025 | VLDB | 4.1905499e-05 |
| 10,839 | Large Language Models for Spatial Analysis Queries | 2025 | VLDB | 4.1905499e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 219 | Deep Entity Matching with Pre-Trained Language Models | 2021 | VLDB | 0.00033354456 |
| 293 | Deep Learning for Entity Matching: A Design Space Exploration | 2018 | SIGMOD | 0.00028661817 |
| 740 | Distributed Representations of Tuples for Entity Resolution | 2018 | VLDB | 0.00017358024 |
| 3,469 | Deep Learning for Blocking in Entity Matching: A Design Space Exploration | 2021 | VLDB | 7.0629476e-05 |
| 5,214 | Dual-Objective Fine-Tuning of BERT for Entity Matching | 2021 | VLDB | 5.6236713e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,338 | Entity Matching: How Similar Is Similar | 2011 | VLDB | 0.00012501156 |
| 318 | Evaluation of entity resolution approaches on real-world match problems | 2010 | VLDB | 0.00027850417 |
| 9,462 | The Battleship Approach to the Low Resource Entity Matching Problem | 2023 | SIGMOD | 4.3324933e-05 |
| 3,469 | Deep Learning for Blocking in Entity Matching: A Design Space Exploration | 2021 | VLDB | 7.0629476e-05 |
| 3,917 | A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs | 2020 | VLDB | 6.6268463e-05 |
| 219 | Deep Entity Matching with Pre-Trained Language Models | 2021 | VLDB | 0.00033354456 |
| 2,758 | A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching | 2020 | SIGMOD | 8.1668285e-05 |
| 293 | Deep Learning for Entity Matching: A Design Space Exploration | 2018 | SIGMOD | 0.00028661817 |
| 6,558 | Pre-trained Embeddings for Entity Resolution: An Experimental Analysis | 2023 | VLDB | 5.0060112e-05 |
| 5,214 | Dual-Objective Fine-Tuning of BERT for Entity Matching | 2021 | VLDB | 5.6236713e-05 |