  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. We motivate our method by
making a connection between learning confidence scores for each rule and
low-rank tensor approximation. DRUM uses bidirectional RNNs to share useful
information across the tasks of learning rules for different relations. We also
empirically demonstrate the efficiency of DRUM over existing rule mining
methods for inductive link prediction on a variety of benchmark datasets.
