@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 {747,
	title = {A Method for Automatically Generating Schema Diagrams for OWL Ontologies},
	booktitle = {1st Iberoamerican Knowledge Graph and Semantic Web Conference (KGSWC)},
	year = {2019},
	month = {06/2019},
	publisher = {Springer},
	organization = {Springer},
	chapter = {149-161},
	address = {Villa Clara, Cuba},
	abstract = {<p>Interest in Semantic Web technologies, including knowledge graphs and ontologies, is increasing rapidly in industry and academics. In order to support ontology engineers and domain experts, it is necessary to provide them with robust tools that facilitate the ontology engineering process. Often, the schema diagram of an ontology is the most important tool for quickly conveying the overall purpose of an ontology. In this paper, we present a method for programmatically generating a schema diagram from an OWL file. We evaluate its ability to generate schema diagrams similar to manually drawn schema diagrams and show that it outperforms VOWL and OWLGrEd. In addition, we provide a prototype implementation of this tool.</p>
},
	keywords = {design patterns, evaluation, implementation, ontology, schema diagrams, visualization},
	author = {Cogan Shimizu and Aaron Eberhart and Nazifa Karima and Quinn Hirt and Adila Krisnadhi 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}
}
