CountTRuCoLa: Rule Confidence Learning for Temporal Knowledge Graph Forecasting
Julia Gastinger, Christian Meilicke, Heiner Stuckenschmidt
9/11/2025
cs.LG
No confident code implementation found for this paper.
Abstract
We address the task of temporal knowledge graph (TKG) forecasting by introducing a fully explainable method based on temporal rules. Motivated by recent work proposing a strong baseline using recurrent facts, our approach learns four simple types of rules with a confidence function that considers both recency and frequency. Evaluated on nine datasets, our method matches or surpasses the performance of eight state-of-the-art models and two baselines, while providing fully interpretable predictions.
