  Learning knowledge graph embedding from an existing knowledge graph is very
important to knowledge graph completion. For a fact $(h,r,t)$ with the head
entity $h$ having a relation $r$ with the tail entity $t$, the current
approaches aim to learn low dimensional representations
$(\mathbf{h},\mathbf{r},\mathbf{t})$, each of which corresponds to the elements
in $(h, r, t)$, respectively. As $(\mathbf{h},\mathbf{r},\mathbf{t})$ is
learned from the existing facts within a knowledge graph, these representations
can not be used to detect unknown facts (if the entities or relations never
occur in the knowledge graph).
  This paper proposes a new approach called TransW, aiming to go beyond the
current work by composing knowledge graph embeddings using word embeddings.
Given the fact that an entity or a relation contains one or more words (quite
often), it is sensible to learn a mapping function from word embedding spaces
to knowledge embedding spaces, which shows how entities are constructed using
human words. More importantly, composing knowledge embeddings using word
embeddings makes it possible to deal with the emerging new facts (either new
entities or relations). Experimental results using three public datasets show
the consistency and outperformance of the proposed TransW.
