DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs
It addresses the need for inductive link prediction that can handle unseen entities and provide human-interpretable explanations, which is incremental as it builds on rule mining approaches.
The paper tackles the problem of learning probabilistic logical rules for inductive and interpretable link prediction on knowledge graphs, proposing DRUM, a scalable and differentiable method that achieves efficiency over existing rule mining methods on benchmark datasets.
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.