@article {986, title = {Deep Deductive Reasoning is a Hard Deep Learning Problem}, journal = {Neurosymbolic Artificial Intelligence}, year = {2025}, author = {Pascal Hitzler and Rushrukh Rayan and Joseph Zalewski and Sanaz Saki Norouzi and Aaron Eberhart and Eugene Y. Vasserman} } @article {969, title = {Deep Deductive Reasoning is a Hard Deep Learning Problem}, year = {2023}, author = {Pascal Hitzler and Rushrukh Rayan and Joseph Zalewski and Sanaz Saki Norouzi and Aaron Eberhart and Eugene Y. Vasserman} } @article {915, title = {Openness and Transparency in Academic Publishing: A Decade of Data from the Semantic Web Journal}, year = {2023}, author = {Pascal Hitzler and Krzysztof Janowicz and Cogan Shimizu and Abhilekha Dalal and Aaron Eberhart and Andrew Eells and Sanaz Saki Norouzi} } @conference {947, title = {Should I Stay or Should I Go: A New Reasoner for Description Logic}, booktitle = {Knowledge Graphs and Semantic Web {\textendash} 5th Iberoamerican Conference and 4th Indo-American Conference}, year = {2023}, author = {Aaron Eberhart and Joseph Zalewski and Pascal Hitzler} } @proceedings {960, title = {Memory Networks for RDFS Reasoning: Experiments}, journal = {SemREC-SMART 2022, Joint Proceedings of SemREC 2022 and SMART 2022 co-located with 21st International SemanticWeb Conference (ISWC 2022)}, year = {2022}, month = {10/2022}, pages = {pp. 28-32}, publisher = {CEUR Workshop Proceedings 3337, CEUR-WS.org 2023}, address = {Hangzhou, China}, author = {Sulogna Chowdhury and Monireh Ebrahimi and Pascal Hitzler and Aaron Eberhart} } @article {897, title = {Neuro-symbolic approaches in artificial intelligence}, journal = {National Science Review}, volume = {9}, year = {2022}, month = {06/2022}, doi = {https://doi.org/10.1093/nsr/nwac035}, url = {https://academic.oup.com/nsr/article/9/6/nwac035/6542460}, author = {Pascal Hitzler and Aaron Eberhart and Monireh Ebrahimi and Md Kamruzzaman Sarker and Lu Zhou} } @article {862, title = {Neuro-Symbolic Artificial Intelligence: Current Trends}, journal = {AI Communications}, volume = {34}, year = {2022}, month = {2021}, chapter = {197}, author = {Md Kamruzzaman Sarker and Lu Zhou and Aaron Eberhart and Pascal Hitzler} } @article {865, title = {On the Capabilities of Pointer Networks for Deep Deductive Reasoning}, journal = {arXiv}, year = {2021}, author = {Monireh Ebrahimi and Aaron Eberhart and Pascal Hitzler} } @conference {860, title = {Expressibility of OWL Axioms with Patterns}, booktitle = {ESWC 2021}, year = {2021}, abstract = {

The high expressivity of the Web Ontology Language (OWL) makes it possible to describe complex relationships between classes, roles, and individuals in an ontology. However, this high expressivity can be an obstacle to correct usage and wide adoption. Past attempts to ameliorate this have included the development of specific, presumably human-friendly syntaxes, such as the Manchester syntax or graphical interfaces for OWL axioms, albeit with limited success. If modelers want to develop suitable OWL axioms it is important to make this as easy as possible. In this paper, we adopt an idea from the Prot{\'e}g{\'e} plug-in, OWLAx, which provides a simple, clickable interface to automatically input axioms of a limited number of types by following simple axiom patterns. In particular, each of these axiom patterns contains at most three classes or roles. We hypothesize that most of the axioms in existing ontologies could be expressed semantically in terms of simple patterns like these, which would mean that more complex patterns can be used very sparingly. Our findings, based on an analysis of 518 ontologies from six public ontology repositories, confirm this hypothesis: Over 90\% of class axioms in the average ontology are indeed expressible with our simple patterns. We provide a detailed analysis of our findings.

}, author = {Aaron Eberhart and Cogan Shimizu and Sulogna Chowdhury and Md Kamruzzaman Sarker and Pascal Hitzler} } @conference {948, title = {Expressibility of OWL Axioms with Patterns}, booktitle = {The Semantic Web {\textendash} 18th International Conference, ESWC 2021}, year = {2021}, pages = {230-245}, author = {Aaron Eberhart and Cogan M Shimizu and Sulogna Chowdhury and Md Kamruzzaman Sarker and Pascal Hitzler} } @conference {856, title = {Neuro-Symbolic Deductive Reasoning for Cross-Knowledge Graph Entailment}, booktitle = {AAAI-MAKE 2021}, year = {2021}, publisher = {AAAI}, organization = {AAAI}, author = {Monireh Ebrahimi and Md Kamruzzaman Sarker and Federico Bianchi and Ning Xie and Aaron Eberhart and Derek Doran and HyeongSik Kim and Pascal Hitzler} } @inbook {814, title = {Seed Patterns for Modeling Trees}, booktitle = {Advances in Pattern-Based Ontology Engineering}, year = {2021}, pages = {48-67}, publisher = {IOS Press}, organization = {IOS Press}, abstract = {Trees {\textendash} i.e., the type of data structure known under this name {\textendash} are central to many aspects of knowledge organization. We investigate some central design choices concerning the ontological modeling of such trees. In particular, we consider the limits of what is expressible in the Web Ontology Language and provide a reusable ontology design pattern for trees.}, author = {Aaron Eberhart and David Carral and Pascal Hitzler and Hilmar Lapp and Sebastian Rudolph} } @proceedings {870, title = {Toward Undifferentiated Cognitive Models}, journal = {International Conference on Cognitive Modeling}, year = {2021}, edition = {19}, abstract = {Autonomous systems are a new frontier for pushing sociotechnical advancement. Such systems will eventually become pervasive, involved in everything from manufacturing, healthcare, defense, and even research itself. However, proliferation is stifled by the high development costs and the resulting inflexibility of the produced systems. The current time needed to create and integrate state of the art autonomous systems that operate as team members in complex situations is a 3-15 year development period, often requiring humans to adapt to limitations in the resulting systems. A new research thrust in interactive task learning (ITL) has begun, calling for natural human-autonomy interaction to facilitate system flexibility and minimize users{\textquoteright} complexity in providing autonomous systems with new tasks. We discuss the development of an undifferentiated agent with a modular framework as a method of approaching that goal.}, author = {Colin Kupitz and Aaron Eberhart and Daniel Schmidt and Christopher Stevens and Cogan Shimizu and Pascal Hitzler and Dario Salvucci and Benji Maruyama and Chris Myers} } @inbook {826, title = {Towards a Modular Ontology for Space Weather Research}, booktitle = {Advances in Ontology Design Patterns}, year = {2021}, publisher = {IOS Press}, organization = {IOS Press}, author = {Cogan Shimizu and Ryan McGranaghan and Aaron Eberhart and Adam C. Kellerman} } @article {855, title = {Towards Bridging the Neuro-Symbolic Gap: Deep Deductive Reasoners}, journal = {Applied Intelligence}, year = {2021}, author = {Monireh Ebrahimi and Aaron Eberhart and Federico Bianchi and Pascal Hitzler} } @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 = {

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.

}, 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 {819, title = {A Domain Ontology for Task Instructions}, booktitle = {KGSWC}, year = {2020}, abstract = { Knowledge graphs and ontologies represent information in a variety of different applications. One use case, the Intelligence, Surveillance, \& Reconnaissance: Mutli-Attribute Task Battery (ISR-MATB), comes from Cognitive Science, where researchers use interdisciplinary methods to understand the mind and cognition. The ISR-MATB is a set of tasks that a cognitive or human agent perform which test visual, auditory, and memory capabilities. An ontology can represent a cognitive agent{\textquoteright}s background knowledge of the task it was instructed to perform and act as an interchange format between different Cognitive Agent tasks similar to ISR-MATB. We present several modular patterns for representing ISR-MATB task instructions, as well as a unified diagram that links them together.}, author = {Aaron Eberhart and Cogan Shimizu and Christopher Stevens and Pascal Hitzler and Christopher W. Myers and Benji Maruyam} } @article {811, title = {A Functional API for OWL}, journal = {The 19th International Semantic Web Conference}, volume = {2721}, year = {2020}, abstract = {We present (f OWL), a minimalistic, functional programming style ontology editor that is based directly on the OWL 2 Structural Specification. (f OWL) is written from scratch, entirely in Clojure, having no other dependencies. Ontologies in (f OWL) are implemented as standalone and homogeneous data structures, which means that the same exact functions written for single axioms or expressions often work identically on any part of an ontology, even the entire ontology itself. The lazy functional style of Clojure also allows for intuitive and simple ontology creation and modification with a minimal memory footprint. All of this is possible without ever needing to use a single class, except of course in the Ontologies one creates!}, author = {Aaron Eberhart and Pascal Hitzler} } @article {823, title = {An Ontology of Instruction 1.0}, year = {2020}, author = {Cogan Shimizu and Pascal Hitzler and Aaron Eberhart and Quinn Hirt and Christopher Stevens and Christopher W. Myers and Benji Maruyama and Colin Kupitz and Dario Salvucci} } @article {821, title = {Towards a Modular Ontology for Space Weather Research}, journal = {CoRR}, volume = {abs/2009.12285}, year = {2020}, url = {https://arxiv.org/abs/2009.12285}, author = {Cogan Shimizu and Ryan McGranaghan and Aaron Eberhart and Adam C. Kellerman} } @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 = {

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.

}, 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} }