DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs

Understand

In this paper, we study the problem of learning probabilistic logical rules for inductive and interpretable link prediction.

• Despite the importance of inductive link prediction, most previous works focused on transductive link prediction and cannot manage previously unseen entities.
• Moreover, they are black-box models that are not easily explainable for humans.
• We propose DRUM, a scalable and differentiable approach for mining first-order logical rules from knowledge graphs which resolves these problems.