  We introduce a new method DOLORES for learning knowledge graph embeddings
that effectively captures contextual cues and dependencies among entities and
relations. First, we note that short paths on knowledge graphs comprising of
chains of entities and relations can encode valuable information regarding
their contextual usage. We operationalize this notion by representing knowledge
graphs not as a collection of triples but as a collection of entity-relation
chains, and learn embeddings for entities and relations using deep neural
models that capture such contextual usage. In particular, our model is based on
Bi-Directional LSTMs and learn deep representations of entities and relations
from constructed entity-relation chains. We show that these representations can
very easily be incorporated into existing models to significantly advance the
state of the art on several knowledge graph prediction tasks like link
prediction, triple classification, and missing relation type prediction (in
some cases by at least 9.5%).
