Robust Discovery of Positive and Negative Rules in Knowledge Bases

By Stefano Ortona, Venkata Vamsikrishna Meduri, Paolo Papotti. 2018

93 citations recorded by the source corpus.

Subfields: Graph Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Graph Learning

1. Naser Ahmadi
2. Mohammed Saeed
3. Paolo Papotti
4. Preslav Nakov

Citations in the corpus

• RuleBERT: Teaching Soft Rules to Pre-Trained Language Models (2021, methodology): “We rely on existing corpora of declarative Horn rules mined from large RDF knowledge bases (KBs) (Galárraga et al., 2015; Ortona et al., 2018; Ahmadi et al., 2020).”
• Explainable Fact Checking with Probabilistic Answer Set Programming (2019, methodology): “ule starts by generating M positive and M negative examples for p. Positive examples are (x;y) entity pairs s.t. p(x;y) 2K, and negative examples are (x;y) pairs that satisfy the following conditions [16]: p(x;y)2= K; there is either some y06=y s.t. p(x;y0) 2K or some x06=x s.t. p(x0;y)2K; there is ”
• RuleHub (2020, methodology): “In Building the corpus, we collected rules from many mining algorithms, but most of the rules come from AMIE [20] and RuDiK [28, 29].”