Analyzing How BERT Performs Entity Matching
Summary: Dissects pre-trained and fine-tuned BERT for entity matching, exposing how fine-tuning alters final layers differently for matching versus nonmatching records. Shows BERT recognizes paired-record structure, while token-level semantic similarity is not central to its decisions. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Matteo Paganelli (University of Modena and Reggio Emilia)
- 2. Francesco Del Buono (University of Modena and Reggio Emilia)
- 3. Andrea Baraldi (University of Modena and Reggio Emilia)
- 4. Francesco Guerra (University of Modena and Reggio Emilia)
BibTeX Citation
@article{paganelli_vldb22,
title = {{Analyzing How BERT Performs Entity Matching}},
author = {Paganelli, Matteo and Del Buono, Francesco and Baraldi, Andrea and Guerra, Francesco},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {8},
pages = {1726--1738},
doi = {10.14778/3529337.3529356},
url = {https://doi.org/10.14778/3529337.3529356},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,580 | Automatic Data Repair: Are We Ready to Deploy? | 2024 | VLDB | 7.2888516e-05 |
| 6,192 | Pre-trained Embeddings for Entity Resolution: An Experimental Analysis | 2023 | VLDB | 5.947284e-05 |
| 9,063 | Deep Active Alignment of Knowledge Graph Entities and Schemata | 2023 | SIGMOD | 5.3251649e-05 |
| 9,381 | Deduplicated Sampling On-Demand | 2025 | VLDB | 5.2755515e-05 |
| 10,186 | Accelerating Approximate Analytical Join Queries over Unstructured Data with Statistical Guarantees | 2026 | SIGMOD | 5.093636e-05 |
| 10,203 | BEACON: Budget-Aware Entity Matching Across Domains | 2026 | SIGMOD | 5.093636e-05 |
| 11,061 | Large Language Models for Spatial Analysis Queries | 2025 | VLDB | 5.093636e-05 |
Previous
Page 1 / 1
Next
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 |
|---|---|---|---|---|
| 141 | Deep Entity Matching with Pre-Trained Language Models | 2021 | VLDB | 0.0002964847 |
| 176 | Deep Learning for Entity Matching: A Design Space Exploration | 2018 | SIGMOD | 0.00027191081 |
| 489 | Distributed Representations of Tuples for Entity Resolution | 2018 | VLDB | 0.0001761456 |
| 2,475 | Deep Learning for Blocking in Entity Matching: A Design Space Exploration | 2021 | VLDB | 8.5277654e-05 |
| 4,447 | Dual-Objective Fine-Tuning of BERT for Entity Matching | 2021 | VLDB | 6.6984471e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 10,203 | BEACON: Budget-Aware Entity Matching Across Domains | 2026 | SIGMOD |
| 2 | 248 | Evaluation of entity resolution approaches on real-world match problems | 2010 | VLDB |
| 3 | 9,610 | The Battleship Approach to the Low Resource Entity Matching Problem | 2023 | SIGMOD |
| 4 | 2,475 | Deep Learning for Blocking in Entity Matching: A Design Space Exploration | 2021 | VLDB |
| 5 | 4,056 | A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs | 2020 | VLDB |
| 6 | 141 | Deep Entity Matching with Pre-Trained Language Models | 2021 | VLDB |
| 7 | 2,463 | A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching | 2020 | SIGMOD |
| 8 | 176 | Deep Learning for Entity Matching: A Design Space Exploration | 2018 | SIGMOD |
| 9 | 6,192 | Pre-trained Embeddings for Entity Resolution: An Experimental Analysis | 2023 | VLDB |
| 10 | 4,447 | Dual-Objective Fine-Tuning of BERT for Entity Matching | 2021 | VLDB |