一种融合实体描述和拓扑结构的知识图谱补全方法  

Knowledge Graph Completion Method Fusing Entity Descriptions and Topological Structure

作　　者：韩道军 李云松[2] 张俊涛 王泽民 HAN Daojun;LI Yunsong;ZHANG Juntao;WANG Zemin(Henan Engineering Research Center of Intelligent Technology and Application,Kaifeng,Henan 475004,China;School of Computer and Information Engineering,Henan University,Kaifeng,Henan 475004,China)

机构地区：[1]河南省智能技术与应用工程技术研究中心,河南开封475004 [2]河南大学计算机与信息工程学院,河南开封475004

出　　处：《计算机科学》2025年第5期260-269,共10页Computer Science

基　　金：河南省高校青年骨干教师基金(2020GGJS027);国家自然科学基金(42371433,62307012);河南省科技攻关项目(232102211056,242102320160)。

摘　　要：知识图谱补全旨在预测给定三元组中缺失的实体和关系,以增强知识图谱的完整性和质量。现有的知识图谱补全方法通常只考虑三元组自身的结构信息或者是实体单一的附加信息(如实体的文本描述或拓扑结构信息),而忽略了融合多种附加信息来增强实体的特征信息,从而导致现有方法补全缺失实体时性能不佳。针对这个问题,提出一种融合实体文本描述和拓扑结构信息的知识图谱补全方法(FuDS-KGC),用于改善知识图谱补全任务的性能。该方法首先通过Transformer和注意力机制提取实体文本描述中特定于关系的特征表示,以增强实体描述的表示特征信息。然后,构建实体的一阶邻居子图,并通过图注意力网络获得实体的拓扑结构特征。最后,设计一种动态门控融合机制,融合实体的文本描述和拓扑结构特征,以增强实体的综合特征表示。在FB15k-237和WN18RR两个数据集上进行实验,实验结果证明了FuDS-KGC的有效性。Knowledge graph completion aims to predict missing entities and relationships in given triplets to enhance the completeness and quality of the knowledge graph.Existing knowledge graph completion methods typically only consider the structural information of triplets or the individual additional information of entities,such as textual descriptions or topological structure information.This overlooks the fusion of multiple types of additional information to enhance entity feature information,leading to suboptimal performance in completing missing entities.To address this issue,this paper proposes a knowledge graph completion method integrating entity text descriptions and topological structure information,referred to as FuDS-KGC,to enhance the performance of knowledge graph completion tasks.This method first extracts relationship-specific feature representations from entity textual descriptions using Transformer and attention mechanisms to enhance the representation feature information of entity descriptions.Next,it constructs first-order neighbor subgraphs for entities and obtains topological structure features through a graph attention network.Finally,a dynamic gated fusion mechanism is designed to integrate entity textual descriptions and topo-logical structure features to enhance the comprehensive feature representation of entities and overcoming the limitation of existing research focusing on the fusion of singular additional information.Experimental results on FB15k-237 and WN18RR datasets demonstrate the effectiveness of FuDS-KGC.

关 键 词：知识图谱补全 TRANSFORMER 实体描述 注意力机制 拓扑结构 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]