Knowledge graphs are large graph-structured databases of facts, which typically suffer from incompleteness. Link prediction is the task of inferring missing relations (links) between entities (nodes) in a knowledge graph. We propose to solve this task by using a hypernetwork architecture to generate convolutional layer filters specific to each relation and apply those filters to the subject entity embeddings. This architecture enables a trade-off between non-linear expressiveness and the number of parameters to learn. Our model simplifies the entity and relation embedding interactions introduced by the predecessor convolutional model, while outperforming all previous approaches to link prediction across all standard link prediction datasets.

翻译：知识图表是大型的图表结构化事实数据库,通常不完全。链接预测是知识图表中判断实体(节点)之间缺少关系(链接)的任务。我们提议通过使用超网络结构来解决这一问题,以产生每个关系特有的递增层过滤器,并将这些过滤器用于主体实体嵌入。这一结构使得非线性表达性和需要学习的参数数量之间能够进行权衡。我们的模型简化了实体和前革命模型引入的嵌入关系的互动,同时超过了以往将预测与所有标准链接预测数据集联系起来的所有方法。