Horse breeding and heritage involve intricate relationships spanning ancestors, descendants, bloodlines, and interdependent connections among horses. These interactions are naturally depicted through reproduction, lineage, crossbreeding to improve traits, genetic enhancement, and various events, forming a complex world of interactions. Effective preservation and management of equine heritage ( \(E\mathcal {H}\) ) necessitate advanced data modeling techniques to capture more complex spatiotemporal dependencies between horses. In this line of research, we propose a system called \(\textsf{Kyle }\) - \(\mathcal{K}\mathcal{G}\) (Knowledge Graph for Equine Heritage), a solution based on graph data modeling principles. To achieve this, we: (i) use a relational model for data sources from the Information Management System for \(E\mathcal {H}\) , (ii) employ a tracker component responsible for extracting and persisting data by mapping relational data to a graph database, and (iii) integrate a visual analytics component designed for decision-makers (e.g., farm managers, breeders). A case study with real-world equine heritage data demonstrates the effectiveness of our approach by providing comprehensive visual analytics through the value of relationships.