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
this https URL

在本研究中，我们提出了一种新的方法，通过在给定知识图谱上应用预训练的实体和关系嵌入来增强知识图谱，并运用规则挖掘系统，从而结合了规则挖掘和基于嵌入的方法的优点，我们在七个基准数据集上进行了大量实验证明了我们的方法的有效性，并提供了开源实现、预训练模型和数据集。