Using a Distributional Semantic Vector Space with a Knowledge Base for
  Reasoning in Uncertain Conditions
  The inherent inflexibility and incompleteness of commonsense knowledge bases
(KB) has limited their usefulness. We describe a system called Displacer for
performing KB queries extended with the analogical capabilities of the word2vec
distributional semantic vector space (DSVS). This allows the system to answer
queries with information which was not contained in the original KB in any
form. By performing analogous queries on semantically related terms and mapping
their answers back into the context of the original query using displacement
vectors, we are able to give approximate answers to many questions which, if
posed to the KB alone, would return no results.
  We also show how the hand-curated knowledge in a KB can be used to increase
the accuracy of a DSVS in solving analogy problems. In these ways, a KB and a
DSVS can make up for each other's weaknesses.
