  Recent work in learning ontologies (hierarchical and partially-ordered
structures) has leveraged the intrinsic geometry of spaces of learned
representations to make predictions that automatically obey complex structural
constraints. We explore two extensions of one such model, the order-embedding
model for hierarchical relation learning, with an aim towards improved
performance on text data for commonsense knowledge representation. Our first
model jointly learns ordering relations and non-hierarchical knowledge in the
form of raw text. Our second extension exploits the partial order structure of
the training data to find long-distance triplet constraints among embeddings
which are poorly enforced by the pairwise training procedure. We find that both
incorporating free text and augmented training constraints improve over the
original order-embedding model and other strong baselines.
