RF-Logic: Scalable Recency-Frequency Logic Rules Learning for Interpretable Temporal Knowledge Graph Forecasting

Temporal knowledge graph forecasting has important applications in recommendation systems, geopolitical forecasting, healthcare, and beyond. Recent works address this task by combining temporal logic rule learning with neural networks. However, existing methods often compromise interpretability when incorporating neural components and do not scale well to large temporal knowledge graphs. In this work, we introduce RF-Logic, a scalable and interpretable model for temporal knowledge graph forecasting that provides explanations through temporal logic rules. Moreover, we introduce two new types of temporal logic rules for temporal knowledge graph forecasting that RF-Logic can express: (i) symmetric shared-object rules and (ii) negation rules. In addition, we propose a dedicated training scheme for RF-Logic that improves generalization while preserving interpretability. To demonstrate the effectiveness of RF-Logic, we evaluate it on TGB 2.0 and show that it performs on par with, or exceeds the state-of-the-art methods across multiple temporal knowledge graph forecasting metrics.