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)

Paper ID: 11681
Venue: VLDB
Year: 2018
Pagerank: 4.613363e-05
Overall Rank: 7,947 | 44.72%
DOI: 10.14778/3229863.3236231

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
11,520	Wikinegata: a Knowledge Base with Interesting Negative Statements	2021	VLDB	4.1945683e-05

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
667	Incremental Knowledge Base Construction Using DeepDive	2015	VLDB	0.00018440557
2,420	From Data Fusion to Knowledge Fusion	2014	VLDB	8.8530994e-05

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