基于相似性负采样的知识图谱嵌入

Knowledge graph embedding based on similarity negative sampling

针对现有知识图谱嵌入模型通过从实体集中随机抽取一个实体来生成负例三元组,导致负例三元组质量较低,影响了实体与关系的特征学习能力。研究了影响负例三元组质量的相关因素,提出了基于实体相似性负采样的方法来生成高质量的负例三元组。在相似性负采样方法中,首先使用K-Means聚类算法将所有实体划分为多个组,然后从正例三元组中头实体所在的簇中选择一个实体替换头实体,并以类似的方法替换尾实体。通过将相似性负采样方法与TransE相结合得到TransE-SNS。研究结果表明:TransE-SNS在链路预测和三元组分类任务上取得了显著的进步。

For the existing knowledge graph embedding model,the random extraction of an entity from the entity set results in the generation of lower-quality negative triples,and this affects the feature learning ability of the entity and the relationship.In this paper,we study the related factors affecting the quality of negative triples,and propose an entity similarity negative sampling method to generate high-quality negative triples.In the similarity negative sampling method,all entities are first divided into a number of groups using the K-means clustering algorithm.Then,corresponding to each positive triple,an entity is selected to replace the head entity from the cluster,whereby the head entity is located in the positive triple,and the tail entity is replaced in a similar approach.TransE-SNS is obtained by combining the similarity negative sampling method with TransE.Experimental results show that TransE-SNS has made significant progress in link prediction and triplet classification tasks.