Unstructured Data Fusion for Schema and Data Extraction
Summary: Introduces SDE: enrich partial query tables from unstructured text by jointly inferring missing schema/data and constructing output tables. End-to-end retrieval + multi-document seq2seq augmentation with interpolation keeps context nearly constant, mitigating retrieval noise and outperforming baselines. (summarized by gpt-5.4-mini on May 24 2026)
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Authors
- 1. Kaiwen Chen (University of Toronto)
- 2. Nick Koudas (University of Toronto)
BibTeX Citation
@inproceedings{chen_sigmod24,
title = {{Unstructured Data Fusion for Schema and Data Extraction}},
author = {Chen, Kaiwen and Koudas, Nick},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3654984},
url = {https://dl.acm.org/doi/10.1145/3654984},
year = {2024}
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 329 | Can Foundation Models Wrangle Your Data? | 2023 | VLDB | 0.00020858443 |
| 377 | TURL: Table Understanding through Representation Learning | 2021 | VLDB | 0.00019570264 |
| 1,038 | ARDA: Automatic Relational Data Augmentation for Machine Learning | 2020 | VLDB | 0.00012370691 |
| 1,988 | SANTOS: Relationship-based Semantic Table Union Search | 2023 | SIGMOD | 9.2482448e-05 |
| 3,013 | Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale | 2022 | SIGMOD | 7.7541678e-05 |
| 3,617 | Leva: Boosting Machine Learning Performance with Relational Embedding Data Augmentation | 2022 | SIGMOD | 7.1574349e-05 |
| 7,197 | Cross Modal Data Discovery over Structured and Unstructured Data Lakes | 2023 | VLDB | 5.5890662e-05 |
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