Fredericks, SidneyBudd, DashiellZheng, SamanthaKim, Junsoo2025-06-042025-06-042025-05https://hdl.handle.net/10919/135049ETDs are Electronic Theses and Dissertation, and this project aims to enhance the storage, accessibility, and exploration of Virginia Tech's ETDs by transforming traditional metadata into a searchable knowledge graph. Recognizing the limitations of flat or relational storage for representing rich academic relationships, we developed a dual-database architecture using Virtuoso (RDF/SPARQL) and Neo4j (property graph/Cypher) to model key entities such as authors, advisors, departments, and disciplines. A Streamlit-based web interface provides an intuitive search experience across both databases, enabling users to explore semantic connections by keyword, year, and entity type. The backend includes a Python-based data pipeline that transforms flat CSV data into normalized graph structures, optimized for batch loading at scale. This framework demonstrates a scalable, future-proof approach to managing large volumes of academic content, supporting more meaningful discovery and long-term preservation of institutional knowledge.en-USETDKnowledge GraphVirtuosoNeo4j