ComHA: 融合几何变换与层次结构的知识图谱嵌入模型

doi:10.19678/j.issn.1000-3428.0069747

摘要/Abstract

摘要：

知识图谱嵌入技术旨在将复杂的语义信息转换为易于计算的低维向量形式，这一过程不仅有助于揭示实体和关系之间的潜在相似性，还能够促进计算机对知识图谱内容的理解和处理。当前，在知识图谱嵌入领域，现有的知识图谱嵌入模型仍然难以建模复杂的关系模式，在对称性、反对称性、反演性以及层次结构等方面仍存在局限性。层次感知模型HAKE通过将实体映射到极坐标系统中，并利用极坐标系中的同心圆来反映不同层次结构，同时捕捉同一层级内实体间的关系，但对于其他复杂关系的建模仍有局限。为了解决这一问题，提出一种新的知识图谱嵌入模型ComHA。ComHA在HAKE的基础上融合了几何变换的思想，通过平移、旋转和缩放操作来增强实体和关系的向量空间表示。在公开数据集WN18、WN18RR、FB15k、FB15k-237和YAGO3-10上的链接预测实验结果表明，ComHA实现了性能提升。这验证了ComHA在捕捉知识图谱中复杂关系和层次结构方面的有效性，为未来的知识图谱嵌入模型设计提供了新的研究方向和研究思路。

关键词: 知识图谱, 知识图谱嵌入, 几何变换, 层次结构感知, 链接预测

Abstract:

Knowledge graph embedding technologies aim to convert complex semantic information into computationally efficient, low-dimensional vector representations. This process not only reveals potential similarities between entities and relationships but also helps a computer understand the content of knowledge graphs and promotes further processing. However, current knowledge graph embedding models face challenges in effectively capturing complex relationship patterns and exhibit limitations in handling aspects such as symmetry, antisymmetry, composition, and hierarchical structures. The hierarchical-aware model, HAKE, addresses this by mapping entities to a polar coordinate system and utilizing concentric circles, thereby capturing relationships between entities at the same level. Nevertheless, constraints remain in modeling other intricate relationships. To overcome these challenges, this study proposes a knowledge graph embedding model called ComHA. Building on the principles of HAKE, ComHA integrates geometric transformation techniques to enhance the vector space representations of entities and relationships by using translation, rotation, and scaling operations. Subsequently, link prediction experiments are conducted on publicly available datasets, including WN18, WN18RR, FB15k, FB15k-237, and YAGO3-10. The results demonstrate significant performance improvements achieved by ComHA. This underscores the effectiveness of ComHA in capturing complex relationships and hierarchical structures within knowledge graphs while providing new research directions and methodological insights for future research in knowledge graph embedding model design.

Key words: knowledge graph, knowledge graph embedding, geometric transformation, hierarchical structure perception, link prediction

李文浩, 张东, 李冠宇. ComHA: 融合几何变换与层次结构的知识图谱嵌入模型[J]. 计算机工程, 2025, 51(11): 123-132.

LI Wenhao, ZHANG Dong, LI Guanyu. ComHA: Knowledge Graph Embedding Model Integrating Geometric Transformations and Hierarchical Structures[J]. Computer Engineering, 2025, 51(11): 123-132.

https://www.ecice06.com/CN/Y2025/V51/I11/123

图/表 11

图1 ComHA模型架构

Fig.1 Architecture of ComHA model

图2 层次感知结构示意图

Fig.2 Schematic diagram of hierarchical perception structure

图3 几何变换结构示意图

Fig.3 Schematic diagram of geometric transformation structure

图4 HAKE和ComHA-J训练的实体嵌入向量可视化效果

Fig.4 Visualization effect of entity embedding vectors trained by HAKE and ComHA-J

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