  We present an implementation of a probabilistic first-order logic called
TensorLog, in which classes of logical queries are compiled into differentiable
functions in a neural-network infrastructure such as Tensorflow or Theano. This
leads to a close integration of probabilistic logical reasoning with
deep-learning infrastructure: in particular, it enables high-performance deep
learning frameworks to be used for tuning the parameters of a probabilistic
logic. Experimental results show that TensorLog scales to problems involving
hundreds of thousands of knowledge-base triples and tens of thousands of
examples.
