Knowledge graphs are data structures that represent entities, their relationships, and organizing principles, providing added value over relational or vector data by extracting multiple facts from a single source, representing structured, unstructured, and semi-structured data, and correlating relationships across different sources. They can also retrieve knowledge several steps away from the original entities in question in a single query, making them useful for applications like GenAI, search engines, real-time fraud detection, and product recommendation engines. Knowledge graphs are implemented as either triple stores or property graphs, with property graphs being easier to use and faster to query compared to RDF. They can be used to implement retrieval-augmented generation (RAG) in GenAI apps by establishing connections between sources and providing more relevant context for LLM queries.