Completion Reasoning Emulation for the Description Logic EL+
Artificial Intelligence
2019-12-12 v1 Logic in Computer Science
Neural and Evolutionary Computing
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.
Cite
@article{arxiv.1912.05063,
title = {Completion Reasoning Emulation for the Description Logic EL+},
author = {Aaron Eberhart and Monireh Ebrahimi and Lu Zhou and Cogan Shimizu and Pascal Hitzler},
journal= {arXiv preprint arXiv:1912.05063},
year = {2019}
}