陈俊帆
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陈俊帆
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论文
Tucker Decomposition with Frequency Attention for Temporal Knowledge Graph Completion
发布时间：2025-10-22点击次数：
发表刊物：
Findings of the Association for Computational Linguistics: ACL 2023
摘要：
Temporal Knowledge Graph Completion aims to complete missing entities or relations under temporal constraints. Previous tensor decomposition-based models for TKGC only independently consider the combination of one single relation with one single timestamp, ignoring the global nature of the embedding. We propose a Frequency Attention (FA) model to capture the global temporal dependencies between one relation and the entire timestamp. Specifically, we use Discrete Cosine Transform (DCT) to capture the frequency of the timestamp embedding and further compute the frequency attention weight to scale embedding. Meanwhile, the previous temporal tucker decomposition method uses a simple norm regularization to constrain the core tensor, which limits the optimization performance. Thus, we propose Orthogonal Regularization (OR) variants for the core tensor, which can limit the non-superdiagonal elements of the 3-rd core tensor. Experiments on three standard TKGC datasets demonstrate that our method outperforms the state-of-the-art results on several metrics. The results suggest that the direct-current component is not the best feature for TKG representation learning. Additional analysis shows the effectiveness of our FA and OR models, even with smaller embedding dimensions.
合写作者：
Likang Xiao,张日崇, Zijie Chen,陈俊帆
论文类型：
国际学术会议
页面范围：
253-265
是否译文：
否
发表时间：
2023-01-01