Mindtagger: A Demonstration of Data Labeling in Knowledge Base Construction
Summary: Mindtagger is a configurable data-labeling tool for debugging and improving DeepDive’s statistical knowledge-base construction pipelines. It operationalizes systematic error-analysis workflows across diverse intermediate data products and labeling tasks. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
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Authors
- 1. Jaeho Shin (Stanford University)
- 2. Christopher Ré (Stanford University)
- 3. Michael Cafarella (University of Michigan)
BibTeX Citation
@article{shin_vldb15,
title = {{Mindtagger: A Demonstration of Data Labeling in Knowledge Base Construction}},
author = {Shin, Jaeho and Ré, Christopher and Cafarella, Michael},
journal = {PVLDB},
series = {{VLDB} '15},
volume = {8},
number = {12},
pages = {1920--1923},
doi = {10.14778/2824032.2824101},
url = {https://doi.org/10.14778/2824032.2824101},
year = {2015}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,410 | Extracting Databases from Dark Data with DeepDive | 2016 | SIGMOD | 6.717496e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 516 | Data Curation at Scale: The Data Tamer System | 2013 | CIDR | 0.00017171198 |
| 579 | Incremental Knowledge Base Construction Using DeepDive | 2015 | VLDB | 0.00016217563 |
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