TY - Generic T1 - Completion Reasoning Emulation for the Description Logic EL+ T2 - Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice Y1 - 2020 A1 - Aaron Eberhart A1 - Monireh Ebrahimi A1 - Lu Zhou A1 - Cogan Shimizu A1 - Pascal Hitzler KW - Deep Learning KW - Description Logic KW - EL+ KW - LSTM KW - NeSy KW - Reasoning AB -

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.

JF - Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice PB - CEUR-WS.org CY - Stanford University, Palo Alto, California, USA VL - 2600 UR - http://ceur-ws.org/Vol-2600/paper5.pdf ER -