A Critical Re-evaluation of Neural Methods for Entity Alignment
Summary: Comparative study of pre-neural and neural EA methods with standardized matching modules; connects EA to record linkage. Paris, a non-neural baseline, beats neural methods across datasets; recommends Paris as baseline and reframes neural positioning. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Manuel Leone (EPFL)
- 2. Stefano Huber (EPFL)
- 3. Akhil Arora (EPFL)
- 4. Alberto García-Durán (EPFL)
- 5. Robert West (EPFL)
BibTeX Citation
@article{leone_vldb22,
title = {{A Critical Re-evaluation of Neural Methods for Entity Alignment}},
author = {Leone, Manuel and Huber, Stefano and Arora, Akhil and García-Durán, Alberto and West, Robert},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {8},
pages = {1712--1725},
doi = {10.14778/3529337.3529355},
url = {https://doi.org/10.14778/3529337.3529355},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,063 | Deep Active Alignment of Knowledge Graph Entities and Schemata | 2023 | SIGMOD | 5.3251649e-05 |
| 9,627 | Making It Tractable to Catch Duplicates and Conflicts in Graphs | 2023 | SIGMOD | 5.2434488e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 65 | Freebase: A Collaboratively Created Graph Database For Structuring Human Knowledge | 2008 | SIGMOD | 0.00038697603 |
| 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 |
| 529 | Magellan: Toward Building Entity Matching Management Systems | 2016 | VLDB | 0.00017096361 |
| 2,086 | PARIS: Probabilistic Alignment of Relations, Instances, and Schema | 2012 | VLDB | 9.1930537e-05 |
| 2,120 | Comparative Analysis of Approximate Blocking Techniques for Entity Resolution | 2016 | VLDB | 9.1406654e-05 |
| 2,252 | Leveraging Data and Structure in Ontology Integration | 2007 | SIGMOD | 8.8675372e-05 |
| 3,235 | Large-Scale Collective Entity Matching | 2011 | VLDB | 7.6139368e-05 |
| 4,056 | A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs | 2020 | VLDB | 6.9344762e-05 |
| 9,119 | Knowledge Translation | 2020 | VLDB | 5.3196675e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 10,248 | Generalized Entity Matching with Adaptivity via Large Language Models | 2026 | SIGMOD |
| 2 | 248 | Evaluation of entity resolution approaches on real-world match problems | 2010 | VLDB |
| 3 | 10,318 | In-context Clustering-based Entity Resolution with Large Language Models: A Design Space Exploration | 2026 | SIGMOD |
| 4 | 9,063 | Deep Active Alignment of Knowledge Graph Entities and Schemata | 2023 | SIGMOD |
| 5 | 176 | Deep Learning for Entity Matching: A Design Space Exploration | 2018 | SIGMOD |
| 6 | 6,192 | Pre-trained Embeddings for Entity Resolution: An Experimental Analysis | 2023 | VLDB |
| 7 | 2,463 | A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching | 2020 | SIGMOD |
| 8 | 4,056 | A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs | 2020 | VLDB |
| 9 | 4,402 | LargeEA: Aligning Entities for Large-scale Knowledge Graphs | 2022 | VLDB |
| 10 | 11,226 | ZeroEA: A Zero-Training Entity Alignment Framework via Pre-Trained Language Model | 2024 | VLDB |