  Large knowledge bases (KBs) are useful for many AI tasks, but are difficult
to integrate into modern gradient-based learning systems. Here we describe a
framework for accessing soft symbolic database using only differentiable
operators. For example, this framework makes it easy to conveniently write
neural models that adjust confidences associated with facts in a soft KB;
incorporate prior knowledge in the form of hand-coded KB access rules; or learn
to instantiate query templates using information extracted from text. NQL can
work well with KBs with millions of tuples and hundreds of thousands of
entities on a single GPU.
