VaxKG: Integrating The Vaccine Ontology And VIOLIN For Advanced Vaccine Queries And LLM-Powered Chat Systems

Vaccine research faces challenges in integrating diverse biomedical datasets. While the Vaccine Investigation and Online Information Network (VIOLIN) provides comprehensive vaccine data, implemented in traditional relational models limit complex analysis. Similarly, the Vaccine Ontology (VO) offers standardized semantic frameworks but lacks comprehensive empirical data. This study addresses these limitations by developing the Vaccine Knowledge Graph (VaxKG) that integrates VIOLIN's dataset with VO's standardized terminology. Using Neo4j, we transformed 12 core VIOLIN tables into a graph structure enriched with VO concepts. The resulting knowledge graph comprises 28,123 VIOLIN data nodes and 101,282 VO resource nodes, connected by 412,865 relationships. Our comparative analysis of Brucella and Influenza vaccines demonstrates VaxKG's ability to enable complex semantic queries and reveal insights unavailable from either resource alone. We further demonstrate VaxKG's utility through VaxChat, a large language model application that leverages the VaxKG as Retrieval-Augmented Generation (RAG) for intuitive vaccine information access.