CERES: Distantly Supervised Relation Extraction from the Semi-Structured Web
Summary: CERES uses distant supervision for relation extraction on semi-structured sites by aligning a knowledge base with site structure. Classifier trained on noisy labels achieves annotation parity, scales to 400k pages and 1.25M facts at ~90% precision. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,243 | Data Integration and Machine Learning: A Natural Synergy | 2018 | VLDB | 4.7913666e-05 |
| 7,826 | The Smallest Extraction Problem | 2021 | VLDB | 4.6416742e-05 |
| 8,751 | Generations of Knowledge Graphs: The Crazy Ideas and the Business Impact | 2023 | VLDB | 4.456315e-05 |
| 9,252 | Improving Information Extraction from Visually Rich Documents using Visual Span Representations | 2021 | VLDB | 4.3690661e-05 |
| 11,256 | Self-Training for Label-Efficient Information Extraction from Semi-Structured Web-Pages | 2023 | VLDB | 4.1945683e-05 |
| 11,543 | Migrating a Privacy-Safe Information Extraction System to a Software 2.0 Design | 2020 | CIDR | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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