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The deductive closure of an ideal knowledge base (KB) contains exactly thelogical queries that the KB can answer. However, in practice KBs are bothincomplete and over-specified, failing to answer some queries that havereal-world answers. \emph{Query embedding} (QE) techniques have been recentlyproposed where KB entities and KB queries are represented jointly in anembedding space, supporting relaxation and generalization in KB inference.However, experiments in this paper show that QE systems may disagree withdeductive reasoning on answers that do not require generalization orrelaxation. We address this problem with a novel QE method that is morefaithful to deductive reasoning, and show that this leads to better performanceon complex queries to incomplete KBs. Finally we show that inserting this newQE module into a neural question-answering system leads to substantialimprovements over the state-of-the-art.

Faithful Embeddings for Knowledge Base Queries