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
Authors
- 1. Alexandros Zeakis (Athena Research Center; National and Kapodistrian University of Athens)
- 2. George Papadakis (National and Kapodistrian University of Athens)
- 3. Dimitrios Skoutas (Athena Research Center)
- 4. Manolis Koubarakis (National and Kapodistrian University of Athens)
BibTeX Citation
@article{zeakis_vldb23,
title = {{Pre-trained Embeddings for Entity Resolution: An Experimental Analysis}},
author = {Zeakis, Alexandros and Papadakis, George and Skoutas, Dimitrios and Koubarakis, Manolis},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {9},
pages = {2225--2238},
doi = {10.14778/3598581.3598594},
url = {https://doi.org/10.14778/3598581.3598594},
year = {2023}
}
Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,501 | TableDC: Deep Clustering for Tabular Data | 2025 | SIGMOD | 5.6029996e-05 |
| 8,860 | Watchog: A Light-weight Contrastive Learning based Framework for Column Annotation | 2023 | SIGMOD | 5.3564029e-05 |
| 9,992 | Progressive Entity Matching: A Design Space Exploration | 2025 | SIGMOD | 5.1815618e-05 |
| 10,131 | Towards Scalable Visual Data Wrangling via Direct Manipulation | 2026 | CIDR | 5.093636e-05 |
| 10,566 | ALER: An Active Learning Hybrid System for Efficient Entity Resolution | 2026 | VLDB | 5.093636e-05 |
| 11,270 | Searching Data Lakes for Nested and Joined Data | 2024 | VLDB | 5.093636e-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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