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The inherent inflexibility and incompleteness of commonsense knowledge bases(KB) has limited their usefulness. We describe a system called Displacer forperforming KB queries extended with the analogical capabilities of the word2vecdistributional semantic vector space (DSVS). This allows the system to answerqueries with information which was not contained in the original KB in anyform. By performing analogous queries on semantically related terms and mappingtheir answers back into the context of the original query using displacementvectors, we are able to give approximate answers to many questions which, ifposed to the KB alone, would return no results.  We also show how the hand-curated knowledge in a KB can be used to increasethe accuracy of a DSVS in solving analogy problems. In these ways, a KB and aDSVS can make up for each other's weaknesses.

Using a Distributional Semantic Vector Space with a Knowledge Base for Reasoning in Uncertain Conditions