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RSRule: Relation-level semantic-driven rule learning for explainable extrapolation on temporal knowledge graphs
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AI
DOI：10.1016/j.inffus.2025.103080.png)
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Abstract
En 中文
Explainability is crucial and valuable for extrapolation reasoning on Temporal Knowledge Graphs (TKGs). By elucidating the reasoning process, we can understand and validate the extrapolation results well, ensuring their validity and reliability. Among various extrapolation methods, rule-based approaches have significant advantages for its explicit rules and explainable reasoning paths. However, current rule-based methods primarily rely on statistics in rule learning, with a heavy dependence on the quantity and quality of the data. In reality, TKGs often suffer from incompleteness and strong sparsity, which severely limits the performance of existing rule-based methods. To address these issues, we propose a novel relation-level semantic-driven rule-based (RSRule) method for explainable extrapolation reasoning, where the relation-level semantics are fused into our rule learning process. Specifically, we concentrate on diverse contextual positional patterns within TKGs and introduce an innovative heterogeneous relation graph to learn relation-level semantics, while employing a relative time encoding to capture the periodic and non-periodic aspects of temporal evolution. Our RSRule focuses on fusing semantic information into the rule learning process, enabling the calculation of rule scores that consider both statistical and semantic aspects. Extensive experiments demonstrate the promising capacity of our RSRule from five aspects, i.e., superiority, improvement, explainability, robustness and generalization.
Keywords:
Temporal knowledge graph
Rule learning
Link prediction
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