Abstract
The ability to enhance deep representations with
prior knowledge is receiving a lot of attention from
the AI community as a key enabler to improve
the way modern Artificial Neural Networks (ANN)
learn. In this paper we introduce our approach
to this task, which comprises of a knowledge extraction algorithm, a knowledge injection algorithm
and a common intermediate knowledge representation as an alternative to traditional neural transfer. As a result of this research, we envisage a
knowledge-enhanced ANN, which will be able to
learn, characterise and reuse knowledge extracted
from the learning process, thus enabling more robust architecture-agnostic neural transfer, greater
explainability and further integration of neural and
symbolic approaches to learning