Knowledge Graph Architectures for Research Analytics and Discovery

Abstract

Knowledge graphs have emerged as a foundational structure for representing, integrating, and analyzing complex scholarly information. By modeling entities, relationships, and contextual attributes explicitly, knowledge graphs support advanced research analytics and discovery workflows that extend beyond traditional bibliographic databases. This article investigates architectural approaches for constructing and operationalizing knowledge graphs in research analytics environments. The study examines design choices related to data ingestion, semantic modeling, graph storage, and analytical services, and evaluates their impact on scalability, interpretability, and discovery effective ness. Through architectural modeling and empirical analysis, the paper provides guidance for designing robust knowledge graph platforms that support exploratory research, decision support, and knowledge discovery.