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
- 2. Nick Koudas
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 513 | TURL: Table Understanding through Representation Learning | 2021 | VLDB | 0.00021288342 |
| 517 | Can Foundation Models Wrangle Your Data? | 2023 | VLDB | 0.00021169035 |
| 1,463 | ARDA: Automatic Relational Data Augmentation for Machine Learning | 2020 | VLDB | 0.00011869295 |
| 3,000 | SANTOS: Relationship-based Semantic Table Union Search | 2023 | SIGMOD | 7.7462128e-05 |
| 3,711 | Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale | 2022 | SIGMOD | 6.823609e-05 |
| 4,967 | Leva: Boosting Machine Learning Performance with Relational Embedding Data Augmentation | 2022 | SIGMOD | 5.7956612e-05 |
| 7,643 | Cross Modal Data Discovery over Structured and Unstructured Data Lakes | 2023 | VLDB | 4.6901105e-05 |
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