Knowledge graph is a structured representation of information as interconnected entities and relationships, enabling AI systems to reason about connections between concepts.

Knowledge graphs go beyond document storage to model how things relate. Instead of an article about "premium subscriptions" and a separate article about "refund policy," a knowledge graph captures that premium_subscription has billing_frequency of monthly, includes feature_access to [list], has associated refund_policy of policy_x, and so on. The AI can traverse these relationships to answer questions requiring synthesis across multiple concepts.

For customer service, knowledge graphs enable more intelligent responses. "What's included in my plan and can I get a refund if I cancel?" requires connecting account information to plan features to cancellation policy to refund rules. A knowledge graph models these connections explicitly rather than relying on the AI to infer them from document proximity.

Building knowledge graphs requires investment in information architecture and ongoing maintenance. The payoff: more accurate, consistent responses to complex questions, and easier updates (change the relationship in one place, not across multiple documents). For organizations with complex products, policies, or regulatory requirements, knowledge graphs provide the structure that document-based retrieval lacks.

Related terms: Knowledge base, Retrieval augmented generation, AI grounding