Knowledge graphs and ontologies are used by tech companies like Google, Amazon, and Apple to organize and search content. They provide an organized way to represent information in a graph structure, which can be queried using topic matching, allowing for more precise answers than traditional keyword search. Knowledge graphs have been used to improve search results, such as the Google Knowledge Graph, which displays a full answer to a query with a "Knowledge Panel" and can also return additional related facts. They are particularly useful in domains like medicine and finance, where complex relationships between pieces of information need to be understood. Concepts play a crucial role in knowledge graphs, enabling them to organize data into topics and create conceptual relationships that help make sense of the complexity of things in the world. While knowledge graphs have their advantages, such as precision and curation, they are limited by their ability to answer complex questions and can be surpassed by Large Language Models (LLMs) for certain use cases.