Knowledge graphs are interlinked sets of facts that describe real-world entities, facts or things and their interrelations in a human understandable form. They offer a way to streamline workflows, automate responses and scale intelligent decisions by providing context for decision support. Knowledge graphs need to be connected around relevant attributes, dynamic, understandable and can combine and uncover connections across silos of information. There are three types of knowledge graphs: context-rich knowledge graphs that incorporate metadata tagging, external-sensing knowledge graphs that aggregate external data sources mapped to internal entities of interest, and natural language processing knowledge graphs that require understanding a company's specific technical terms and nuances of human language. Knowledge graphs provide context for decision support, help ensure answers are appropriate to the situation, and have applications in search, customer support, document classification, supply chain risk, market activity segregation, sales opportunities, improved search, chatbot implementation, and more.