  In artificial intelligence (AI), knowledge is the information required by an
intelligent system to accomplish tasks. While traditional knowledge bases use
discrete, symbolic representations, detecting knowledge encoded in the
continuous representations learned from data has received increasing attention
recently. In this work, we propose a method for building a continuous knowledge
base (CKB) that can store knowledge imported from multiple, diverse neural
networks. The key idea of our approach is to define an interface for each
neural network and cast knowledge transferring as a function simulation
problem. Experiments on text classification show promising results: the CKB
imports knowledge from a single model and then exports the knowledge to a new
model, achieving comparable performance with the original model. More
interesting, we import the knowledge from multiple models to the knowledge
base, from which the fused knowledge is exported back to a single model,
achieving a higher accuracy than the original model. With the CKB, it is also
easy to achieve knowledge distillation and transfer learning. Our work opens
the door to building a universal continuous knowledge base to collect, store,
and organize all continuous knowledge encoded in various neural networks
trained for different AI tasks.
