Fouille de règles différentielles causales dans les graphes de connaissances
In EGC 2021, vol. RNTI-E-37, pp.293-300
In this paper, we present an approach that discovers differential causal rules in Knowledge Graphs. Such rules express that for two class instances, a different treatment leads to different outcomes. The proposed approach is based on a semantic matching and strata that can be defined as complex sub-classes. A first experimental evaluation on a DBPedia extract showed that such discovered rules can help to explain a significant number of variability in outcomes.