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Diffusion Model with Selective Attention for Temporal Knowledge Graph Reasoning

delete2026-01-01
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PRE
AI
R
Rushan Geng
C
Chen Ge
C
Cuicui Luo *
DOI：10.1007/978-3-032-05981-9_9delete
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摘要

摘要

En 中文
时空知识图谱推理旨在预测未来时间步缺失的实体，作为一项关键任务，近年来因其出色地捕捉历史关联和预测未来事件的能力而受到广泛关注。尽管现有方法（如图学习与逻辑规则）已部分解决了该问题，但在建模未来事件的不确定性方面仍存在局限，尤其是在预测罕见或未见事实时。为应对这些挑战，我们提出了一种基于选择性注意力机制的扩散模型（DMSA）用于时空知识图谱推理。在本文方法中，编码器通过选择性注意力机制强调关键信息，而扩散模块引入噪声以增强模型预测未见事件的能力。通过将选择性注意力与扩散模块相结合，我们的模型在提升记忆能力的同时，也增强了预测未来未见事件的能力。在五个公开数据集上的实验结果表明，我们提出的模型在多个评估指标上均取得了最先进的表现。
Keyword:
Temporal knowledge graph
Temporal knowledge graph reasoning
Diffusion model
Selective attention

期刊

M
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES. RESEARCH TRACK, ECML PKDD 2025, PT II
IF:
0
论文数:
28
被引数:
0

机构

U
university of chinese academy of sciences, cas
学者数:
4.1W
论文数: 3.8W
被引数: 75
C
chinese academy of sciences
学者数:
56.7W
论文数: 45.0W
被引数: 704
引用论文

引用论文

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Benchmark and Neural Architecture for Conversational Entity Retrieval from a Knowledge Graph
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errZamiri,Mona; Qiang,Yao; Nikolaev,Fedor; Zhu,Dongxiao; Kotov,Alexander
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