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FuST-KGC: Fusing sub-graph structures and textual semantics for knowledge graph completion

delete2026-01-12
delete0
PRE
AI
D
Daojun Han
M
Mengxin Jin
J
Juntao Zhang *
L
Linkun Fan
Q
Qinglin Su
B
Bendong Qiao
DOI：10.1016/j.neucom.2026.132690delete
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摘要

摘要

En 中文
• 我们提出FuST-KGC，一种融合子图结构和文本语义的知识图谱补全新方法。 • 我们引入一种动态的、度引导的采样策略，用于构建信息丰富的局部子图，有效捕获关键推理路径。 • FuST-KGC将多跳子图拓扑线性化为文本序列，使单一预训练语言模型能够融合结构上下文与细粒度语义。 • FuST-KGC解决了现有模型简化图结构或使用复杂独立编码器的局限性，提升了推理准确性和语义-结构整合性。

期刊

Neurocomputing 封面图
Neurocomputing
IF:
6.5
论文数:
2.5W
被引数:
6.5W

机构

H
Henan University
学者数:
2.0K
论文数: 658
被引数: 4
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