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
Authors
- 1. Colin Lockard (University of Washington)
- 2. Xin Luna Dong (Amazon)
- 3. Arash Einolghozati (Meta)
- 4. Prashant Shiralkar (Amazon)
BibTeX Citation
@article{lockard_vldb18,
title = {{CERES: Distantly Supervised Relation Extraction from the Semi-Structured Web}},
author = {Lockard, Colin and Dong, Xin Luna and Einolghozati, Arash and Shiralkar, Prashant},
journal = {PVLDB},
series = {{VLDB} '18},
volume = {11},
number = {10},
pages = {1084--1096},
doi = {10.14778/3231751.3231758},
url = {https://doi.org/10.14778/3231751.3231758},
year = {2018}
}
Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,415 | Data Integration and Machine Learning: A Natural Synergy | 2018 | VLDB | 5.5332662e-05 |
| 7,972 | The Smallest Extraction Problem | 2021 | VLDB | 5.4150414e-05 |
| 8,176 | Improving Information Extraction from Visually Rich Documents using Visual Span Representations | 2021 | VLDB | 5.3835163e-05 |
| 9,082 | Generations of Knowledge Graphs: The Crazy Ideas and the Business Impact | 2023 | VLDB | 5.2283159e-05 |
| 11,767 | Self-Training for Label-Efficient Information Extraction from Semi-Structured Web-Pages | 2023 | VLDB | 4.9793485e-05 |
| 12,043 | Migrating a Privacy-Safe Information Extraction System to a Software 2.0 Design | 2020 | CIDR | 4.9793485e-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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