Universal schema jointly embeds knowledge bases and textual patterns to reason about entities and relations for automatic knowledge base construction and information extraction. In the past, entity pairs and relations were represented as learned vectors with compatibility determined by a scoring function, limiting generalization to unseen text patterns and entities. Recently, 'column-less' versions of Universal Schema have used compositional pattern encoders to generalize to all text patterns. In this work we take the next step and propose a 'row-less' model of universal schema, removing explicit entity pair representations. Instead of learning vector representations for each entity pair in our training set, we treat an entity pair as a function of its relation types. In experimental results on the FB15k-237 benchmark we demonstrate that we can match the performance of a comparable model with explicit entity pair representations using a model of attention over relation types. We further demonstrate that the model per- forms with nearly the same accuracy on entity pairs never seen during training.

该研究提出了一种新的Universal Schema模型，该模型不再使用显式的实体对表示，而是通过关系类型的注意力机制，将实体对视为关系类型的函数，并在实验结果中展示了该模型的性能可媲美使用显式实体对表示的模型，且几乎在训练时未见过的实体对上表现出几乎相同的准确性。

无行通用模式