A knowledge graph is an organized representation of real-world entities and their relationships, typically stored in a graph database. It captures the context and meaning of how these entities are connected, allowing for deeper data insights and applications such as generative AI, fraud detection, and master data management. Knowledge graphs can be built with a small scope to solve specific problems, and they offer flexibility, performance, and developer-friendly code compared to other approaches like triple stores or relational databases. They provide a powerful tool for storing and organizing data to enable more sophisticated understanding of that data, and various use cases include generative AI, fraud detection, master data management, supply chain management, investigative journalism, and drug discovery in healthcare research. To get started with knowledge graphs, one can download a free guide or explore learning resources such as Semantics workshops on GitHub, Ontology-Based Reasoning 101, and GraphAcademy for knowledge graph fundamentals using a property graph model in the Neo4j Graph Database.