  We consider the problem of embedding entities and relations of knowledge
bases in low-dimensional vector spaces. Unlike most existing approaches, which
are primarily efficient for modeling equivalence relations, our approach is
designed to explicitly model irreflexive relations, such as hierarchies, by
interpreting them as translations operating on the low-dimensional embeddings
of the entities. Preliminary experiments show that, despite its simplicity and
a smaller number of parameters than previous approaches, our approach achieves
state-of-the-art performance according to standard evaluation protocols on data
from WordNet and Freebase.
