{"ID":2872221,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.09474","arxiv_id":"2509.09474","title":"CountTRuCoLa: Rule Confidence Learning for Temporal Knowledge Graph Forecasting","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.","short_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 frequ...","url_abs":"https://arxiv.org/abs/2509.09474","url_pdf":"https://arxiv.org/pdf/2509.09474v1","authors":"[\"Julia Gastinger\",\"Christian Meilicke\",\"Heiner Stuckenschmidt\"]","published":"2025-09-11T13:56:21Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}