@proceedings {770,
	title = {Completion Reasoning Emulation for the Description Logic EL+},
	journal = {Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice},
	volume = {2600},
	year = {2020},
	month = {03/2020},
	publisher = {CEUR-WS.org},
	address = {Stanford University, Palo Alto, California, USA},
	abstract = {<p>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{\textquoteright}s and LSTM{\textquoteright}s predictions on corrupt data with correct answers.</p>
},
	keywords = {Deep Learning, Description Logic, EL+, LSTM, NeSy, Reasoning},
	url = {http://ceur-ws.org/Vol-2600/paper5.pdf},
	author = {Aaron Eberhart and Monireh Ebrahimi and Lu Zhou and Cogan Shimizu and Pascal Hitzler}
}
@conference {748,
	title = {Pseudo-Random ALC Syntax Generation},
	booktitle = {The Semantic Web: ESWC 2018 Satellite Events - ESWC 2018 Satellite Events, Heraklion, Crete, Greece, June 3-7, 2018, Revised Selected Papers},
	volume = {11155},
	year = {2018},
	pages = {19{\textendash}22},
	publisher = {Springer},
	organization = {Springer},
	address = {Heraklion, Crete, Greece},
	abstract = {We discuss a tool capable of rapidly generating pseudo-random syntactically valid ALC expression trees. The program is meant to allow a researcher to create large sets of independently valid expressions with a minimum of personal bias for experimentation.},
	keywords = {ALC, Description Logic, DL, random generation, synthetic data},
	isbn = {978-3-319-98191-8},
	doi = {10.1007/978-3-319-98192-5\_4},
	url = {https://doi.org/10.1007/978-3-319-98192-5\_4},
	author = {Aaron Eberhart and Michelle Cheatham and Pascal Hitzler}
}
