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Uncertainty Management in Rule-Based Information Extraction Systems

Summary: Proposes a probabilistic, max-entropy model to quantify uncertainty in rule-based information extraction and its compositional rules. Adds scalable learning via model decomposition; enables incremental accuracy as new rules or data are added. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4174
Venue
SIGMOD
Year
2009
Pagerank
6.9810029e-05
Overall Rank
3,973 | 72.75%
DOI
10.1145/1559845.1559858

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{michelakis_sigmod09,
        title = {{Uncertainty Management in Rule-Based Information Extraction Systems}},
        author = {Michelakis, Eirinaios and Krishnamurthy, Rajasekar and Haas, Peter J. and Vaithyanathan, Shivakumar},
        series = {{SIGMOD} '09},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1559845.1559858},
        url = {https://dl.acm.org/doi/10.1145/1559845.1559858},
        year = {2009}
}

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