This tutorial will introduce foundational elements of symbolic artificial intelligence, namely knowledge graphs and ontologies. In a knowledge graph, entities are represented as nodes, and the relationships between them are represented as edges. Knowledge graphs offer powerful query answering over structured and unstructured data sources, often with a need for high data quality and limited deductive properties. Ontologies complement data-focused knowledge graphs in that they formalise entities and their relations in a manner that is amenable to automated reasoning, so as to make additional inferences and uncover modelling errors. The lecture will cover developments in biomedicine, where substantial efforts have been made to construct and exploit structured knowledge for understanding human health and disease.