The generative AI market has seen significant growth, leading to increased demand for knowledge graphs. Knowledge graphs are semantically rich data models that store entities, relationships, and an organizing principle to capture meta-information about core concepts. Two popular approaches to building knowledge graphs are RDF (Resource Description Framework) and property graphs. RDF is a W3C standard for data exchange on the Web, but it has limitations, such as not being able to model multiple distinct relationships of the same type. Property graphs, on the other hand, offer flexibility and scalability, allowing for fast querying and traversal, and are designed specifically as a database model. They have advantages over RDF, including simplicity, detailed data storage, interoperability, and standards compliance. The property graph model has been successfully implemented in various industries, such as NASA, Basecamp Research, and Novo Nordisk, to solve complex problems, reduce costs, and achieve breakthroughs. When choosing between a knowledge graph approach, it is recommended to use the property graph model by default and layer in organizing principles from the RDF world when needed.