TransE-KCB:一种改进负样本采样的知识图谱表示方法OA北大核心CSTPCD

In order to solve the shortcomings of randomly generated negative samples in the translation model,to generate high-quality negative samples and improve the training effect of the model,the paper proposes an improved knowledge representation learning model for negative sample sampling,which is called TransE-KCB.The model introduced the K-Means++clustering algorithm to form different types of similarity entity clusters.5 entities in the cluster were randomly selected,and the similarity with the replaced entity was calculated.The highest ranked entity was selected and replaced with the replaced entity.On this basis,in order to solve the problem of"false negatives",this paper introduced a Bloom filter to filter"false negatives".The experimental results show that,compared with TransE and other models,the TransE-KCB model has better model expression ability,and the knowledge representation ability has been greatly improved.