Document Type

Conference Proceeding

Publication Date

2020

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 εℒ+ 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.

Comments

This work is licensed under CC BY 4.0 Creative Commons Attribution 4.0 License

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