  Automated claim checking is the task of determining the veracity of a claim
given evidence found in a knowledge base of trustworthy facts. While previous
work has taken the knowledge base as given and optimized the claim-checking
pipeline, we take the opposite approach - taking the pipeline as given, we
explore the choice of knowledge base. Our first insight is that a
claim-checking pipeline can be transferred to a new domain of claims with
access to a knowledge base from the new domain. Second, we do not find a
"universally best" knowledge base - higher domain overlap of a task dataset and
a knowledge base tends to produce better label accuracy. Third, combining
multiple knowledge bases does not tend to improve performance beyond using the
closest-domain knowledge base. Finally, we show that the claim-checking
pipeline's confidence score for selecting evidence can be used to assess
whether a knowledge base will perform well for a new set of claims, even in the
absence of ground-truth labels.
