An Exploration of Explainable Machine Learning Using Semantic Web Technology. - Tyler Procko; Timothy Elvira; Omar Ochoa; Nicholas Del Rio (ICSC, 2022)

GPT-4: A Stochastic Parrot or Ontological Craftsman? Discovering Implicit Knowledge Structures in Large Language Models. - Tyler Thomas Procko; Timothy Elvira; Omar Ochoa (TransAI, 2023)

Automatic Generation of BFO-Compliant Aristotelian Definitions in OWL Ontologies with GPT. - Tyler Thomas Procko; Omar Ochoa; Timothy Elvira (TransAI, 2023)

An analysis of explainability methods for convolutional neural networks. - Lynn Vonder Haar; Timothy Elvira; Omar Ochoa (Eng. Appl. Artif. Intell., 2023)

Exploring Testing Methods for Large Language Models. - Timothy Elvira; Tyler Thomas Procko; Lynn Vonderhaar; Omar Ochoa (ICMLA, 2024)

Measuring the Impact of Scene Level Objects on Object Detection: Towards Quantitative Explanations of Detection Decisions. - Lynn Vonder Haar; Timothy Elvira; Luke Newcomb; Omar Ochoa (CoRR, 2024)

Towards Robust Training Datasets for Machine Learning with Ontologies: A Case Study for Emergency Road Vehicle Detection. - Lynn Vonderhaar; Timothy Elvira; Tyler Procko; Omar Ochoa (CoRR, 2024)

Dawn of the dialogue: AI's leap from lab to living room. - Tyler Procko; Timothy Elvira; Omar Ochoa (Frontiers Artif. Intell., 2024)

Generating and Verifying Synthetic Datasets with Requirements Engineering. - Lynn Vonderhaar; Timothy Elvira; Omar Ochoa (CAIN, 2025)

Exploring Requirements Engineering for Machine Learning via a Product Case Study. - Lynn Vonderhaar; Timothy Elvira; Juan Ortiz Couder; Omar Ochoa (FLAIRS, 2025)

Measuring the Impact of Scene Level Objects: A Novel Method for Quantitative Explanations. - Lynn Vonderhaar; Timothy Elvira; Omar Ochoa (FLAIRS, 2025)

Creating Robust Data Sets for AI by Leveraging Ontological Structures. - Lynn Vonderhaar; Timothy Elvira; Tyler Thomas Procko; Omar Ochoa (ICSC, 2025)