CERTEM: Explaining and Debugging Black-box Entity Resolution Systems with CERTA
Summary: CERTEM brings CERTA-based interpretability to black-box, deep-learning entity resolution. It combines attribute-level saliency with counterfactual value examples to explain predictions and debug state-of-the-art ER models on benchmark data. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
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Authors
- 1. Tommaso Teofili (Red Hat; Roma Tre University)
- 2. Donatella Firmani (Sapienza University)
- 3. Nick Koudas (University of Toronto)
- 4. Paolo Merialdo (Roma Tre University)
- 5. Divesh Srivastava (AT&T)
BibTeX Citation
@article{teofili_vldb22,
title = {{CERTEM: Explaining and Debugging Black-box Entity Resolution Systems with CERTA}},
author = {Teofili, Tommaso and Firmani, Donatella and Koudas, Nick and Merialdo, Paolo and Srivastava, Divesh},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {12},
pages = {3642--3645},
doi = {10.14778/3554821.3554864},
url = {https://doi.org/10.14778/3554821.3554864},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,878 | Can we trust LLM Self-Explanations for Entity Resolution? | 2026 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 134 | Deep Entity Matching with Pre-Trained Language Models | 2021 | VLDB | 0.00030043481 |
| 158 | Deep Learning for Entity Matching: A Design Space Exploration | 2018 | SIGMOD | 0.00028046388 |
| 457 | Distributed Representations of Tuples for Entity Resolution | 2018 | VLDB | 0.00017907103 |
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