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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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 |
|---|---|---|---|---|
| 397 | TURL: Table Understanding through Representation Learning | 2021 | VLDB | 0.00019278189 |
| 420 | Can Foundation Models Wrangle Your Data? | 2023 | VLDB | 0.00018789852 |
| 1,121 | ARDA: Automatic Relational Data Augmentation for Machine Learning | 2020 | VLDB | 0.00012093059 |
| 2,315 | SANTOS: Relationship-based Semantic Table Union Search | 2023 | SIGMOD | 8.7605101e-05 |
| 3,304 | Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale | 2022 | SIGMOD | 7.5404346e-05 |
| 4,735 | Leva: Boosting Machine Learning Performance with Relational Embedding Data Augmentation | 2022 | SIGMOD | 6.5315782e-05 |
| 7,572 | Cross Modal Data Discovery over Structured and Unstructured Data Lakes | 2023 | VLDB | 5.5938952e-05 |
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