  The deductive closure of an ideal knowledge base (KB) contains exactly the
logical queries that the KB can answer. However, in practice KBs are both
incomplete and over-specified, failing to answer some queries that have
real-world answers. \emph{Query embedding} (QE) techniques have been recently
proposed where KB entities and KB queries are represented jointly in an
embedding space, supporting relaxation and generalization in KB inference.
However, experiments in this paper show that QE systems may disagree with
deductive reasoning on answers that do not require generalization or
relaxation. We address this problem with a novel QE method that is more
faithful to deductive reasoning, and show that this leads to better performance
on complex queries to incomplete KBs. Finally we show that inserting this new
QE module into a neural question-answering system leads to substantial
improvements over the state-of-the-art.
