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Temporal knowledge graph multi-hop path reasoning method based on reinforcement learning
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AI
DOI：10.1016/j.neucom.2025.131067.png)
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Abstract
En 中文
Temporal Knowledge Graphs (TKGs) extend traditional knowledge graphs by incorporating temporal information, enabling reasoning over time-dependent facts. However, many real-world knowledge graphs are incomplete, requiring effective reasoning methods to infer missing information and improve their overall quality. Multi-hop reasoning is a promising method for this task, but existing methods often fail to fully utilize temporal information and relationship modeling, resulting in less accurate inference. Additionally, many approaches lack interpretability, making it difficult to explicitly trace the reasoning pathways. To address these challenges, this paper proposes reinforcement learning-based multi-hop path reasoning for TKGs (RLPR), a novel model designed to enhance inference accuracy and interpretability by integrating temporal and relational information. RLPR introduces a timestamp decomposition strategy to better capture temporal dependencies while reducing model complexity. Unlike conventional entity-focused attention mechanisms, RLPR applies attention at the relationship level, allowing for more precise modeling of relational interactions. Furthermore, RLPR employs a reinforcement learning-based strategy network that explicitly constructs multi-hop reasoning pathways, improving both the interpretability and adaptability of the inference process. Compared to SOAT methods, the RLPR model achieved the best results on open-source datasets. For the link prediction results, the metrics improved by 15.07 %, 13.56 %, 4.26 % on the ICEWS14 datasets. When the path length setting for the RLPR model during reasoning is set to 2, the RLPR model improved various metrics on the ICEWS14–2 datasets by 13.04 %, 60.61 %, 38.67 %, and 27.08 %.
Keywords:
Temporal Knowledge Graphs
Multi-hop Reasoning
Reinforcement Learning
Link Prediction
Interpretable AI
Journal
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6.5
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2.5W
Citations:
6.5W