提高长尾数据知识图谱补全性能的一种新算法  

A new algorithm for improving the completion performance of knowledge graph of long-tail data

作　　者：何苗惠 段旭祥 吴至友 HE Miaohui;DUAN Xuxiang;WU Zhiyou(School of Mathematical Sciences,Chongqing Normal University,Chongqing 401331,China)

机构地区：[1]重庆师范大学数学科学学院,重庆401331

出　　处：《运筹学学报(中英文)》2025年第1期41-54,共14页Operations Research Transactions

基　　金：国家自然科学基金(No.11871128)。

摘　　要：知识图谱是众多智能应用中一种重要的语义数据,但其数据的不完备性给实际应用带来了很多困难,因此需要对知识图谱中缺失的语义信息进行补全。知识图谱嵌入是知识图谱补全的重要方法之一,这类方法通常在非长尾数据情况下具有较好的效果,但在长尾数据情况下其效果较差。由于非长尾数据的语义较丰富,为了提升长尾数据情况下知识图谱补全效果,本文将非长尾数据作为监督知识迁移到长尾数据中,提出了一种新的算法——融入期望最大化算法思想的双重嵌入方法,来改进长尾数据的知识图谱补全性能,进而提高其实际应用效果。通过在FB15K数据集中进行链接预测任务的对比实验,实验结果表明本文提出的融入期望最大化算法思想的双重嵌入方法效果较好。Knowledge graph is an important semantic data in many intelligent applications,but the incompleteness of its data brings many difficulties to practical applications.Therefore,it is necessary to complete the missing semantic information in the knowledge graph.Knowledge graph embedding is one of the important methods of knowledge graph completion.This type of method usually has better results in the case of non-long-tail data,but its effect is poor in the case of long-tail data.Since the semantics of non-long-tail data is richer,in order to improve the effect of knowledge graph completion in the case of long-tail data,this paper transfers non-long-tail data as supervised knowledge to long-tail data,and proposes a new algorithm—the dual embedding method with the idea of expectation maximization algorithm,to improve the completion performance of knowledge graph,and its practical application effect.Through the comparative experiment of link prediction task in FB15K data set,the experimental results show that the dual embedding method with the idea of expectation maximization algorithm proposed in this paper is effective.

关 键 词：知识图谱补全 知识图谱嵌入 期望最大化算法 双重嵌入方法 

分 类 号：O224[理学—运筹学与控制论] TP181[理学—数学]