Publication Date
2025
Document Type
Thesis
Committee Members
Cogan Shimizu, Ph.D. (Advisor); Wen Zhang, Ph.D. (Committee Member); Krishnaprasad Thirunarayan, Ph.D. (Committee Member)
Degree Name
Master of Science (MS)
Abstract
Explainability, interpretability and adaptability (EIA) remain three central motivations for next-generation Artificial Intelligence (AI), especially as Large Language Models (LLMs) continue to engage with ever-increasing knowledge bodies. As the landscape pushes toward controllable agentic Retrieval-Augmented Generation (RAG) systems where AI agents engage in iterative, guided reasoning, a critical question arises as to the extent to which the knowledge design itself shapes these models' reasoning behavior. This work conducts a systematic evaluation of how different conceptualizations and representation of the identical knowledge affect an LLM's path-based reasoning capabilities. Through the introduction of controlled variations along graph structural complexity, linguistic and semantic design dimensions, the study isolates how design factors propagate through the interpretation and reasoning pipeline. Findings posit ontology engineering as a first-order determinant and illustrate how decisions made during knowledge construction may have direct implications for stability and alignment of future neurosymbolic architectures.
Page Count
104
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2025
Copyright
Copyright 2025, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
ORCID ID
0009-0000-2287-1198
