DISSERTATION DEFENSE

Author : Hong Yung (Joey) Yip
Advisors: Dr. Amit Sheth
Date: April 20th, 2026
Time: 9:30 am
Place: 529 Seminar Room, AI Institute
Link:  https://sc-edu.zoom.us/j/8440139296?omn=81104593412

Abstract

Modern Artificial Intelligence (AI) systems, particularly Large Language Models (LLMs), are powerful but remain largely black-box systems whose outputs often lack explicit semantics, provenance, and temporal validity. In high-stakes enterprise and regulatory settings, these limitations reduce trust, auditability, and reliability. This dissertation argues that data alone is not enough and presents a Neuro-symbolic AI framework (EMPWR) for the Knowledge Graph (KG) lifecycle that integrates symbolic knowledge representation with data-driven learning to improve interoperability, traceability, and grounded response quality.

The dissertation makes four primary contributions, each aligned with a phase of the KG lifecycle. First, for data interoperability, it introduces an ontology-driven framework for modeling non-sequential processes and integrating fragmented data through a Sales Engagement Graph (SEG). Second, for knowledge representation, it introduces the Dynamic-Singleton Property Graph (D-SPG) which combines the semantic rigor of Resource Description Framework (RDF) with the efficiency of Property Graphs (PGs) while modeling provenance, attribution, and temporal validity as first-class elements. The model preserves SPARQL-compliant querying while enabling auditable, metadata-rich knowledge representation.

Third, for alignment and enrichment, the dissertation proposes Context-Enriched Learning Models that leverage hierarchical graph structure, source synonyms, and semantic groups in the UMLS Metathesaurus to improve biomedical vocabulary alignment. Fourth, for consumption and evaluation, it introduces VERIFY, the Validated Evidence Retrieval & Integrity Framework for High-Fidelity Medical Device Question Answering over FDA medical device records. VERIFY contributes a retrieval-aware metric, Response Fidelity (RF), which measures LLM response on correctness, grounding, omission, evidence integrity, and structural validity. These contributions establish EMPWR as an end-to-end framework for building, maintaining, and evaluating trustworthy KG-grounded AI systems, demonstrating that explicit domain knowledge and principled knowledge representation are essential for reliable AI in high-stakes settings.