A knowledge graph is a type of knowledge base that uses a graph-structured data model or topology to integrate data. It represents a network of real-world entities, such as objects, events, situations, or concepts, and illustrates the relationships between them. A knowledge graph is made up of three main components: nodes, edges, and labels. Any object, place, or person can be a node, and an edge defines the relationship between the nodes.

Knowledge graphs are often used to store and integrate data, and they are associated with linked open data projects, search engines, knowledge-engines, question-answering services, and social networks. They are used to answer factual questions and provide immediate and factual answers to search queries about real-world entities and their relationships to one another.

A knowledge graph is a collection of interlinked descriptions of entities that put data into context and enable data integration, analytics, and sharing. It combines characteristics of knowledge representation and reasoning, and it can include an ontology that allows both humans and machines to understand and reason about its contents.

The definitions of knowledge graphs vary, but most definitions view the topic through a Semantic Web lens and include features such as interlinked entity descriptions, formal structure and semantics, and the ability to represent knowledge as concepts and the relationships between them.

Knowledge graphs have applications in data and information-heavy services, intelligent content and package reuse, responsive and contextually aware content recommendation, knowledge graph-powered drug discovery, semantic search, investment market intelligence, information discovery in regulatory documents, advanced drug safety, and more.