  Knowledge Bases (KBs) require constant up-dating to reflect changes to the
world they represent. For general purpose KBs, this is often done through
Relation Extraction (RE), the task of predicting KB relations expressed in text
mentioning entities known to the KB. One way to improve RE is to use KB
Embeddings (KBE) for link prediction. However, despite clear connections
between RE and KBE, little has been done toward properly unifying these models
systematically. We help close the gap with a framework that unifies the
learning of RE and KBE models leading to significant improvements over the
state-of-the-art in RE. The code is available at
https://github.com/billy-inn/HRERE.
