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Automatic Rule Refinement for Information Extraction

Summary: Applies tuple-lineage techniques from data provenance to diagnose incorrect rule-based information extractions. Ranks candidate rule modifications from labeled correct/incorrect outputs, reducing iterative refinement effort in SystemT. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
10298
Venue
VLDB
Year
2010
Pagerank
6.5235308e-05
Overall Rank
4,752 | 67.40%
DOI
10.14778/1920841.1920916

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{liu_vldb10,
        title = {{Automatic Rule Refinement for Information Extraction}},
        author = {Liu, Bin and Chiticariu, Laura and Chu, Vivian and Jagadish, H.V. and Reiss, Frederick R.},
        journal = {PVLDB},
        series = {{VLDB} '10},
        volume = {3},
        number = {1},
        pages = {588--597},
        doi = {10.14778/1920841.1920916},
        url = {https://doi.org/10.14778/1920841.1920916},
        year = {2010}
}

Incoming Citations (Sorted by Pagerank)

Showing 9 of 9 citing papers.

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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
17 Provenance Semirings 2007 PODS 0.00059843817
319 Declarative Information Extraction Using Datalog with Embedded Extraction Predicates 2007 VLDB 0.00021377065
385 Why Not? 2009 SIGMOD 0.00019455743
628 On the Provenance of Non-Answers to Queries over Extracted Data 2008 VLDB 0.00015630285
2,032 Explaining Missing Answers to SPJUA Queries 2010 VLDB 9.2828822e-05
3,216 Toward Best-Effort Information Extraction 2008 SIGMOD 7.6322141e-05
7,239 I4E: Interactive Investigation of Iterative Information Extraction 2010 SIGMOD 5.6645117e-05
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