  Methods based on representation learning currently hold the state-of-the-art
in many natural language processing and knowledge base inference tasks. Yet, a
major challenge is how to efficiently incorporate commonsense knowledge into
such models. A recent approach regularizes relation and entity representations
by propositionalization of first-order logic rules. However,
propositionalization does not scale beyond domains with only few entities and
rules. In this paper we present a highly efficient method for incorporating
implication rules into distributed representations for automated knowledge base
construction. We map entity-tuple embeddings into an approximately Boolean
space and encourage a partial ordering over relation embeddings based on
implication rules mined from WordNet. Surprisingly, we find that the strong
restriction of the entity-tuple embedding space does not hurt the
expressiveness of the model and even acts as a regularizer that improves
generalization. By incorporating few commonsense rules, we achieve an increase
of 2 percentage points mean average precision over a matrix factorization
baseline, while observing a negligible increase in runtime.
