  This paper proposes a novel approach for relation extraction from free text
which is trained to jointly use information from the text and from existing
knowledge. Our model is based on two scoring functions that operate by learning
low-dimensional embeddings of words and of entities and relationships from a
knowledge base. We empirically show on New York Times articles aligned with
Freebase relations that our approach is able to efficiently use the extra
information provided by a large subset of Freebase data (4M entities, 23k
relationships) to improve over existing methods that rely on text features
alone.
