RuDiK: Rule Discovery in Knowledge Bases
Summary: RuDiK discovers rules over KBs, enabling positive rules to infer facts and negative rules to flag inconsistencies. Deployed on Yago, DBpedia, Freebase, WikiData; robust to errors, achieving 85–97% accuracy, with a demo for KB curation and ML training data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Stefano Ortona (Meltwater)
- 2. Venkata Vamsikrishna Meduri (Arizona State University)
- 3. Paolo Papotti (EURECOM)
BibTeX Citation
@article{ortona_vldb18,
title = {{RuDiK: Rule Discovery in Knowledge Bases}},
author = {Ortona, Stefano and Meduri, Venkata Vamsikrishna and Papotti, Paolo},
journal = {PVLDB},
series = {{VLDB} '18},
volume = {11},
number = {12},
pages = {1946--1949},
doi = {10.14778/3229863.3236231},
url = {https://doi.org/10.14778/3229863.3236231},
year = {2018}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,718 | Wikinegata: a Knowledge Base with Interesting Negative Statements | 2021 | VLDB | 5.093636e-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 |
|---|---|---|---|---|
| 579 | Incremental Knowledge Base Construction Using DeepDive | 2015 | VLDB | 0.00016217563 |
| 2,351 | From Data Fusion to Knowledge Fusion | 2014 | VLDB | 8.7099618e-05 |
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