  We present a new approach to integrating deep learning with knowledge-based
systems that we believe shows promise. Our approach seeks to emulate reasoning
structure, which can be inspected part-way through, rather than simply learning
reasoner answers, which is typical in many of the black-box systems currently
in use. We demonstrate that this idea is feasible by training a long short-term
memory (LSTM) artificial neural network to learn EL+ reasoning patterns with
two different data sets. We also show that this trained system is resistant to
noise by corrupting a percentage of the test data and comparing the reasoner's
and LSTM's predictions on corrupt data with correct answers.
