语义区分和子图整合驱动的知识图谱表示学习方法

张杰勇; 尚斌; 杨易春; 刘彬; 刘东洋; 王鹏; 刘均

doi:10.7652/xjtuxb202607018

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语义区分和子图整合驱动的知识图谱表示学习方法

更新时间：2026-07-09

- 语义区分和子图整合驱动的知识图谱表示学习方法
- Knowledge Graph Representation Learning Driven by Semantic Discrimination and Subgraph Integration
- 西安交通大学学报 2026年60卷第7期页码：196-206
- 作者机构：
  
  空军工程大学信息与导航学院，710077，西安
  西安市环境监测站，710018，西安
  西安交通大学计算机科学与技术学院，710049，西安
- 作者简介：
  
  作者简介：张杰勇（1983—），男，副教授，硕士生导师；
  尚斌（通信作者），男，讲师。
- 基金信息：
  
  国家自然科学基金资助项目(62293553)
- DOI：10.7652/xjtuxb202607018
  中图分类号： TP391
- 收稿：2025-12-05，
  
  纸质出版：2026-07-10
- 稿件说明：
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张杰勇, 尚斌, 杨易春, 等. 语义区分和子图整合驱动的知识图谱表示学习方法[J]. 西安交通大学学报, 2026,60(7):196-206.

ZHANG Jieyong, SHANG Bin, YANG Yichun, et al. Knowledge Graph Representation Learning Driven by Semantic Discrimination and Subgraph Integration[J]. Journal of Xi'an Jiaotong University, 2026, 60(7): 196-206.
张杰勇, 尚斌, 杨易春, 等. 语义区分和子图整合驱动的知识图谱表示学习方法[J]. 西安交通大学学报, 2026,60(7):196-206. DOI： 10.7652/xjtuxb202607018.

ZHANG Jieyong, SHANG Bin, YANG Yichun, et al. Knowledge Graph Representation Learning Driven by Semantic Discrimination and Subgraph Integration[J]. Journal of Xi'an Jiaotong University, 2026, 60(7): 196-206. DOI： 10.7652/xjtuxb202607018.

摘要

针对现有基于图神经网络的知识图谱表示学习方法难以区分实体的多样化语义，以及如何在嵌入学习过程中对所有关系进行均质化处理的问题，提出了一种新颖的基于语义区分和子图整合知识图谱表示学习方法，通过层次化建模，分阶段挖掘图谱结构信息以学习实体表示。语义区分模块通过构建多个关系感知子图并整合其语义信息，从而全面捕获实体属性。子图整合模块则通过评估不同子图的重要性，实施选择性邻域聚合以保留具有区分度的语义特征。此外，采用编码器对知识图谱三元组进行建模并执行链接预测任务，并引入交叉熵损失函数训练模型，以充分整合语义与结构信息。实验结果表明：所提方法在3个标准数据集上相较于现有最优模型实现了全面性能提升。在FB15 K-237数据集上，平均倒数排名（

）、命中率Hits@3和Hits@10（正确答案出现在预测排名前3、前10位的比例）分别提升了2.42%、3.90%和1.44%；在YAGO3-10数据集上，

、Hits@3和Hits@10分别提升了2.17%、4.81%和2.01%；在WN18RR数据集上，

、Hits@3和Hits@10分别提升了0.61%、0.59%和0.86%，有效验证了所提方法的优越性。

Abstract

To address the limitations of existing graph neural network-based knowledge graph representation learning methods in distinguishing diverse entity semantics and handling relations homogeneously during embedding learning，a novel knowledge graph representation learning method based on semantic discrimination

and subgraph integration is proposed. Hierarchical modeling is employed to learn entity representations by progressively mining structural information from the graph.In the semantic discrimination module，multiple relation-aware subgraphs are constructed and their semantic information is integrated to capture comprehensive entity attributes. In the subgraph integration module，the importance of different subgraphs is evaluated，and selective neighborhood aggregation is performed to preserve discriminative semantic features. Additionally，knowledge graph triples are modeled via an encoder for link prediction tasks，and a cross-entropy loss function is introduced during model training to effectively integrate semantic and structural information.Experimental results on three standard datasets demonstrate that the proposed method yields comprehensive performance improvements over current state-of-the-art models. Specifically，on the FB15 K-237 dataset，improvements of 2.42%，3.90%，and 1.44% were achieved in mean reciprocal rank（

），Hits@3，and Hits@10（denoting the proportion of queries with the correct answer in the topk predictions（

=3，10））respectively.On the YAGO3-10 dataset，the

，Hits@3，and Hits@10 were improved by 2.17%，4.81%，and 2.01%，respectively.On the WN18 RR dataset，increases of 0.61%，0.59%，and 0.86% were observed in the corresponding metrics.The superiority of the proposed method is thus validated.

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references

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