Holistic Query Evaluation over Information Extraction Pipelines
Summary: Holistic in-database query processing for information extraction pipelines models the joint CRF distribution with factor graphs. Canopy Factor Graphs, built on canopy clustering, capture this distribution and enable approximate cross-task inference. (summarized by gpt-5-nano on Feb 09 2026)
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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 |
|---|---|---|---|---|
| 228 | Reference Reconciliation in Complex Information Spaces | 2005 | SIGMOD | 0.00032266415 |
| 468 | MauveDB: Supporting Model-based User Views in Database Systems | 2006 | SIGMOD | 0.00022407392 |
| 668 | Incremental Knowledge Base Construction Using DeepDive | 2015 | VLDB | 0.00018428925 |
| 1,015 | Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS | 2011 | VLDB | 0.00014630577 |
| 1,408 | Entity Resolution with Iterative Blocking | 2009 | SIGMOD | 0.00012162791 |
| 1,567 | Querying Continuous Functions in a Database System | 2008 | SIGMOD | 0.00011320794 |
| 2,186 | Scalable Probabilistic Databases with Factor Graphs and MCMC | 2010 | VLDB | 9.3460817e-05 |
| 3,626 | On-the-Fly Entity-Aware Query Processing in the Presence of Linkage | 2010 | VLDB | 6.9003552e-05 |
| 4,960 | Querying Probabilistic Information Extraction | 2010 | VLDB | 5.7992639e-05 |
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