Differential Causal Rules Mining in Knowledge Graphs

Abstract

In recent years, keen interest towards Knowledge Graphs has increased in both academia and the industry which has led to the creation of various datasets and the development of different research topics. In this paper, we present an approach that discovers differential causal rules in Knowledge Graphs. Such rules express that for two different class instances, a different treatment leads to different outcomes. Discovering causal rules is often the key of experiments, independently of their domain. The proposed approach is based on semantic matching relying on community detection and strata that can be defined as complex sub-classes. An experimental evaluation on two datasets shows that such mined rules can help gain insights into various domains.