  Rule mining on knowledge graphs allows for explainable link prediction.
Contrarily, embedding-based methods for link prediction are well known for
their generalization capabilities, but their predictions are not interpretable.
Several approaches combining the two families have been proposed in recent
years. The majority of the resulting hybrid approaches are usually trained
within a unified learning framework, which often leads to convergence issues
due to the complexity of the learning task. In this work, we propose a new way
to combine the two families of approaches. Specifically, we enrich a given
knowledge graph by means of its pre-trained entity and relation embeddings
before applying rule mining systems on the enriched knowledge graph. To
validate our approach, we conduct extensive experiments on seven benchmark
datasets. An analysis of the results generated by our approach suggests that we
discover new valuable rules on the enriched graphs. We provide an open source
implementation of our approach as well as pretrained models and datasets at
https://github.com/Jean-KOUAGOU/EnhancedRuleLearning
