Title | Completion Reasoning Emulation for the Description Logic EL+ |
Publication Type | Conference Proceedings |
Year of Publication | 2020 |
Authors | Eberhart, A, Ebrahimi, M, Zhou, L, Shimizu, C, Hitzler, P |
Conference Name | Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice |
Volume | 2600 |
Date Published | 03/2020 |
Publisher | CEUR-WS.org |
Conference Location | Stanford University, Palo Alto, California, USA |
Keywords | Deep Learning, Description Logic, EL+, LSTM, NeSy, Reasoning |
Abstract | 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. |
URL | http://ceur-ws.org/Vol-2600/paper5.pdf |