A knowledge graph is a data structure that represents what we know about part of the real world, enabling search algorithms to provide better responses by understanding relationships between terms and instances of facts and concepts. It can be used in computing for various applications, including machine learning, natural language processing, and search. Redis Enterprise's RedisGraph module can help load and use ontologies when building knowledge graph infrastructures, allowing users to query the ontology structure via Cypher queries and visualize it with RediSearch. Knowledge graphs have been used in various domains, such as retail and medicine, to extend the hand-curated aspects of an ontology into instances of data that relate to items within the ontology. They can be used to provide recommendations, summarize information, and support professionals in clinical settings or for drug development by carefully constructing the knowledge graph. Additionally, knowledge graphs do not have to be completely realized as graphs and can be combined with other data structures, such as tabular data stores, to store instance data and enable model inference and full-text searches. The possibilities for implementing and using knowledge graphs within Redis are widespread, and users can try out ontologies and knowledge graphs on various platforms, including Docker, Redis Enterprise, and the Redis Cloud Pro service.