@booklet {990,
	title = {An Ontology for Representing Curriculum and Learning Material},
	year = {2025},
	abstract = {Educational, learning, and training materials have become extremely commonplace across the Internet. Yet, they frequently remain disconnected from each other, fall into platform silos, and so on. One way to overcome this is to provide a mechanism to integrate the material and provide cross-links across topics.
In this paper, we present the Curriculum KG Ontology, which we use as a framework for the dense interlinking of educational materials, by first starting with organizational and broad pedagogical principles. We provide a materialized graph for the Prototype Open Knowledge Network use-case, and validate it using competency questions sourced from domain experts and educators. },
	keywords = {education, modular ontology design, ontology},
	doi = {10.48550/arXiv.2506.05751},
	url = {https://arxiv.org/abs/2506.05751},
	author = {Antrea Christou and Chris Davis Jaldi and Joseph Zalewski and Hande Kucuk McGinty and Pascal Hitzler and Cogan Shimizu}
}
@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}
}
