知识图谱中基于多关系路径的链路预测方法  

Link prediction method based on multi relation path in knowledge map

作　　者：袁华兵 刘敏 杨延庆 Yuan Huabing;Liu Min;Yang Yanqing(Information Technology Department,Xi'an Medical College,Xi'an 710021,China;School of Mathematics and Computer Science,Shaanxi University of Technology,Hanzhong 723001,China)

机构地区：[1]西安医学院信息技术处,西安710021 [2]陕西理工大学数学与计算机科学学院,汉中723001

出　　处：《电子测量技术》2021年第5期135-138,共4页Electronic Measurement Technology

基　　金：陕西省自然科学基金(2019JQ-927)项目资助。

摘　　要：针对基于单一关系路径的链路预测方法无法挖掘知识图谱中不同路径之间影响的问题,提出了一种基于多关系路径的链路预测方法。首先,采用基于路径信息的相似性指标来计算所有关系路径之间的相似度。然后,将不同路径之间的关系投影延伸至新的路径投影和路径约束,并采用随机梯度下降执行训练过程,从而能够在隐式空间中通过低维表示学习筛选出不同路径之间的显式特征。在安然邮件数据集和美国国家自然科学基金会数据集上进行了验证分析。实验结果表明,相比于其他多种路径链路预测算法,该算法在MAP和AUC指标上的最大提升幅度约20%,表现出更高的预测精度。In order to solve the problem that the link prediction method based on single relational path cannot mine the influence of different paths in the knowledge map, a link prediction method based on multi relational path is proposed. Firstly, the similarity index based on path information is used to calculate the similarity between all relational paths. Then, the relationship projection between different paths is extended to the new path projection and path constraints, and the training process is performed by using random gradient descent, so that the explicit features between different paths can be screened out through low dimensional representation learning in implicit space. The validation analysis is carried out on Enron email data set and National Natural Science Foundation data set. Experimental results show that, compared with other path link prediction algorithms, the maximum improvement of MAP and AUC is about 20%, showing higher prediction accuracy.

关 键 词：链路预测 知识图谱 多关系路径 路径投影 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]