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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)

Paper ID
11791
Venue
VLDB
Year
2018
Pagerank
5.9150411e-05
Overall Rank
6,320 | 56.64%
DOI
10.14778/3231751.3231758

Incoming Non-self Citations Over Time

Authors

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}
}

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