  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.
