山西大学学报（自然科学版）2026，Vol.49Issue(4)：571-580,10.DOI:10.13451/j.sxu.ns.2025116
面向少样本知识图谱补全的关系感知语义建模方法
Relation-aware Semantic Modeling Approach for Few-shot Knowledge Graph Completion
摘要
Abstract
Few-shot knowledge graph completion aims to infer unseen queries for a target relation with only a few support triples.This study proposed a relation-aware semantic modeling method.It integrated global structural context and local neighborhood se-mantics of triples to create expressive relation-specific representations.The method used a context path generator to extract multi-hop dependency paths.It also employed a multi-scale graph encoder to capture global semantic structure and local entity features.A semantic matching aggregation module aligned query triples with support triples under each relation.Experiments on NELL-One and Wiki-One datasets showed consistent improvement over existing methods on multiple metrics.For the five-shot setting on NELL-One,it increased mean reciprocal rank,Hits@10,Hits@5,and Hits@1 by 3.8%,2.4%,2.2%,and 4.3%,respectively.The re-sults demonstrate the method's effectiveness and robustness in handling sparse neighborhoods and relation ambiguity in few-shot sce-narios.关键词
少样本知识图谱补全/上下文建模/多尺度表示学习/关系语义表示Key words
few-shot knowledge graph completion/context modeling/multi-scale representation learning/relation semantic represen-tation分类
信息技术与安全科学引用本文复制引用
胡杨娟,汪凯乐,杨飞,李玉玲..面向少样本知识图谱补全的关系感知语义建模方法[J].山西大学学报（自然科学版）,2026,49(4):571-580,10.基金项目
国家自然科学基金(62406007 （）
62401016) （）
