%0 Conference Proceedings %B Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice %D 2020 %T Completion Reasoning Emulation for the Description Logic EL+ %A Aaron Eberhart %A Monireh Ebrahimi %A Lu Zhou %A Cogan Shimizu %A Pascal Hitzler %K Deep Learning %K Description Logic %K EL+ %K LSTM %K NeSy %K Reasoning %X

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.

%B Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice %I CEUR-WS.org %C Stanford University, Palo Alto, California, USA %V 2600 %8 03/2020 %G eng %U http://ceur-ws.org/Vol-2600/paper5.pdf