Provenance-based Dictionary Refinement in Information Extraction
Summary: Provenance of extraction outputs drives dictionary refinement, formulating an optimization to maximize quality by pruning entries. Efficient algorithms with a probabilistic model for incomplete labeling are proposed and validated on real extractors, with implications for view maintenance in relational settings. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Sudeepa Roy
- 2. Laura Chiticariu
- 3. Vitaly Feldman
- 4. Frederick R. Reiss
- 5. Huaiyu Zhu
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 31 | Provenance Semirings | 2007 | PODS | 0.00078516827 |
| 289 | Declarative Information Extraction Using Datalog with Embedded Extraction Predicates | 2007 | VLDB | 0.00028865317 |
| 655 | On Propagation of Deletions and Annotations Through Views | 2002 | PODS | 0.00018593536 |
| 1,316 | Harvesting Relational Tables from Lists on the Web | 2009 | VLDB | 0.00012616422 |
| 2,600 | Tracing Data Errors with View-Conditioned Causality | 2011 | SIGMOD | 8.4652308e-05 |
| 2,974 | Efficiently Incorporating User Feedback into Information Extraction and Integration Programs | 2009 | SIGMOD | 7.7890486e-05 |
| 3,315 | Computing Query Probability with Incidence Algebras | 2010 | PODS | 7.2247513e-05 |
| 3,479 | Toward Best-Effort Information Extraction | 2008 | SIGMOD | 7.053581e-05 |
| 4,973 | Maximizing Conjunctive Views in Deletion Propagation | 2011 | PODS | 5.7882537e-05 |
| 6,527 | Automatic Rule Refinement for Information Extraction | 2010 | VLDB | 5.0200332e-05 |
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