基于卷积神经网络的知识图谱补全方法研究

知识图谱是事实三元组的集合,其表示形式为(头实体,关系,尾实体).为了补全知识图谱中缺失的实体和关系,提出一种基于卷积神经网络的知识图谱补全方法.使用传统嵌入模型训练三元组,得到实体向量和关系向量;将三元组表示成3列矩阵,作为卷积神经网络的输入,卷积后得到三元组的特征表示图;连接所有特征图和权重向量进行点乘得到每个三元...

KNOWLEDGE GRAPH COMPLETION METHOD BASED ON CONVOLUTIONAL NEURAL NETWORK

Knowledge graph is a collection of factual triples,the representation is(head,relation,tail).In order to complete the missing entities and relations in the knowledge graph,an improved convolutional neural network knowledge graph completion method is proposed.The traditional embedding model was used to train the triples to obtain the entity vector and the relation vector;the triple was represented as a 3-column matrix as the input to the convolutional neural network,and the matrix was convolved with convolution kernel to obtain feature maps;all feature maps were connected,and the weight vector was multiplied to obtain the score of each triple to determine the correctness of a triple.In the experiment,the data sets WN18RR,FB15K-237,FB15K were used to link prediction and triple classification experiments.The experimental results show that compared with other methods,the proposed method achieves better experimental results on the Mean Rank and Hit@10 indicators,which proves that the method can effectively improve the prediction accuracy of the triples.