CountTRuCoLa: Rule Confidence Learning for Temporal Knowledge Graph Forecasting
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915490114306048 |
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| author | Gastinger, Julia Meilicke, Christian Stuckenschmidt, Heiner |
| author_facet | Gastinger, Julia Meilicke, Christian Stuckenschmidt, Heiner |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_09474 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CountTRuCoLa: Rule Confidence Learning for Temporal Knowledge Graph Forecasting Gastinger, Julia Meilicke, Christian Stuckenschmidt, Heiner Machine Learning 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. |
| title | CountTRuCoLa: Rule Confidence Learning for Temporal Knowledge Graph Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.09474 |