  We present a novel extension to embedding-based knowledge graph completion
models which enables them to perform open-world link prediction, i.e. to
predict facts for entities unseen in training based on their textual
description. Our model combines a regular link prediction model learned from a
knowledge graph with word embeddings learned from a textual corpus. After
training both independently, we learn a transformation to map the embeddings of
an entity's name and description to the graph-based embedding space. In
experiments on several datasets including FB20k, DBPedia50k and our new dataset
FB15k-237-OWE, we demonstrate competitive results. Particularly, our approach
exploits the full knowledge graph structure even when textual descriptions are
scarce, does not require a joint training on graph and text, and can be applied
to any embedding-based link prediction model, such as TransE, ComplEx and
DistMult.
