@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 {10,
	title = {DistEL: A Distributed EL+ Ontology Classifier},
	booktitle = {Proceedings of the 9th International Workshop on Scalable Semantic Web Knowledge Base Systems, co-located with the International Semantic Web Conference (ISWC 2013)},
	volume = {1046},
	year = {2013},
	month = {10/2013},
	pages = {17-32},
	publisher = {CEUR-WS.org},
	organization = {CEUR-WS.org},
	address = {Sydney, Australia},
	abstract = {OWL 2 EL ontologies are used to model and reason over data from diverse domains such as biomedicine, geography and road traffic. Data in these domains is increasing at a rate quicker than the increase in main memory and computation power of a single machine. Recent efforts in OWL reasoning algorithms lead to the decrease in classification time from several hours to a few seconds even for large ontologies like SNOMED CT. This is especially true for ontologies in the description logic EL+ (a fragment of the OWL 2 EL profile). Reasoners such as Pellet, Hermit, ELK etc. make an assumption that the ontology would fit in the main memory, which is unreasonable given projected increase in data volumes. Increase in the data volume also necessitates an increase in the computation power. This lead us to the use of a distributed system, so that memory and computation requirements can be spread across machines. We present a distributed system for the classification of EL+ ontologies along with some results on its scalability and performance.},
	keywords = {Classification, DistEL, Distributed Reasoning, EL+, OWL, Scalability},
	author = {Raghava Mutharaju and Pascal Hitzler and Prabhaker Mateti},
	editor = {Thorsten Liebig and Achille Fokoue}
}
