Table Overlap Estimation through Graph Embeddings
Summary: Armadillo uses graph neural networks to learn table representations. Cosine similarity between embeddings estimates the overlap ratio; introduces GitTables- and Wikipedia-based datasets (1.32M pairs) and yields speedups over Sloth with strong accuracy. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Francesco Pugnaloni (Hasso Plattner Institute; University of Potsdam)
- 2. Luca Zecchini (University of Modena and Reggio Emilia)
- 3. Matteo Paganelli (University of Modena and Reggio Emilia)
- 4. Matteo Lissandrini (University of Verona)
- 5. Felix Naumann (Hasso Plattner Institute; University of Potsdam)
- 6. Giovanni Simonini (University of Modena and Reggio Emilia)
BibTeX Citation
@inproceedings{pugnaloni_sigmod25,
title = {{Table Overlap Estimation through Graph Embeddings}},
author = {Pugnaloni, Francesco and Zecchini, Luca and Paganelli, Matteo and Lissandrini, Matteo and Naumann, Felix and Simonini, Giovanni},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725365},
url = {https://dl.acm.org/doi/10.1145/3725365},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,287 | Shape-Agnostic Table Overlap Discovery: A Maximum Common Subhypergraph Approach | 2026 | SIGMOD | 5.093636e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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