结合时间映射和卷积神经网络的知识补全

Knowledge base Completion based on Temporal Mapping and CNN

现有知识库存在大量缺失事实且事实常携带时间信息。针对主流嵌入表示方法在知识补全时常忽略时间维度的问题,设计一种时间敏感的三元组嵌入表示方法TSKGC(time sensitive knowledge graph completion)。通过为时间戳分配超平面,将时序信息合并到实体关系空间中,并进一步将映射后三元组的3列k维矩阵表示用作卷积神经网络的输入,在不同超平面对应的多通道中并行处理,提取三元组特征用于知识补全。在YAGO11k和Wikidata12k数据集上的实验证明,TSKGC具备一定的时间预测能力,并能有效利用时间信息提高链路预测的性能表现,特别在1-M、M-1和M-M复杂关系类型上相比主流模型具备一定优势。

There are a lot of missing facts in the existing knowledge inventory,and the facts often carry time information.Aiming at the problem that the time dimension is often ignored in the knowledge completion of mainstream embedded representation methods,a time sensitive knowledge graph completion method(TSKGC) is designed.By assigning hyperplanes to the timestamp,the time series information is merged into the entity relation space,and the three-column kdimensional matrix representation of the mapped triple is further used as the input of the convolution neural network(CNN).In parallel processing in multi-channels corresponding to different hyperplanes,and triplet features are extracted for knowledge completion.Experiments on YAGO11 k and Wikidatal2 k datasets show that TSKGC has a certain time prediction ability,and can effectively use time information to improve the performance of link prediction,especially in1-M,M-1 and M-M complex relationship types have certain advantages compared with mainstream models.