Incremental Knowledge Base Construction Using DeepDive
Summary: DeepDive fuses DB and ML ideas to accelerate knowledge-base construction from unstructured data. Proposes incremental inference via sampling and variational methods, plus a rule-based optimizer, achieving 100x speedups with negligible quality impact on 5 KBC workloads. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jaeho Shin (Stanford University)
- 2. Sen Wu (Stanford University)
- 3. Feiran Wang (Stanford University)
- 4. Christopher De Sa (Stanford University)
- 5. Ce Zhang (Stanford University; University of Wisconsin)
- 6. Christopher Ré (Stanford University)
BibTeX Citation
@article{shin_vldb15,
title = {{Incremental Knowledge Base Construction Using DeepDive}},
author = {Shin, Jaeho and Wu, Sen and Wang, Feiran and De Sa, Christopher and Zhang, Ce and Ré, Christopher},
journal = {PVLDB},
series = {{VLDB} '15},
volume = {8},
number = {11},
pages = {1310--1321},
doi = {10.14778/2809975.2809984},
url = {https://doi.org/10.14778/2809975.2809984},
year = {2015}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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