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arXiv · 1912.05063

Completion Reasoning Emulation for the Description Logic EL+

Aaron Eberhart·Monireh Ebrahimi·Lu Zhou·Cogan Shimizu·Pascal Hitzler
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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.

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Aaron Eberhart, Monireh Ebrahimi, Lu Zhou, Cogan Shimizu, Pascal Hitzler. 2019-12-11. Completion Reasoning Emulation for the Description Logic EL+. https://arxiv.org/abs/1912.05063

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