Knowledge graphs are graphical representations of large databases of facts, which typically suffer from incompleteness. Inferring missing relations (links) between entities (nodes) is the task of link prediction. A recent state-of-the-art approach to link prediction, ConvE, implements a convolutional neural network to extract features from concatenated subject and relation vectors. Whilst results are impressive, the method is unintuitive and poorly understood. We propose a hypernetwork architecture that generates simplified relation-specific convolutional filters that (i) outperforms ConvE and all previous approaches across standard datasets; and (ii) can be framed as tensor factorization and thus set within a well established family of factorization models for link prediction. We thus demonstrate that convolution simply offers a convenient computational means of introducing sparsity and parameter tying to find an effective trade-off between non-linear expressiveness and the number of parameters to learn.
翻译:知识图表是大量事实数据库的图形表示,这些数据库通常不完全。推断实体(节点)之间缺少关系(链接)是链接预测的任务。最近的一种最先进的将预测联系起来的方法是ConvE, 实施一个革命性神经网络,从融合的物体和关系矢量中提取特征。虽然结果令人印象深刻,但这种方法不直观,而且不易理解。我们提议了一个超网络结构,产生简化的特定关系过滤器,以便(一) 超越ConvE和标准数据集之间以往的所有方法;以及(二) 能够作为高压因子化框架,从而在既定的参数组合模型中设置,用于链接预测。因此,我们证明,聚合只是提供了一种方便的计算手段,即引入宽度和参数,以便在非线性表达性和需要学习的参数数量之间找到有效的权衡。