Architecture

Knowledge Graph

A knowledge graph is a structured representation of entities and their relationships that enables AI systems to reason about connected information — used for search, recommendations, and decision-making.

What Is a Knowledge Graph?

A knowledge graph is a network of entities (people, products, concepts) and the relationships between them, stored in a way that enables machines to traverse connections and reason about related information. Google's Knowledge Graph, for example, powers those information panels you see in search results.

How Do Businesses Use Knowledge Graphs?

Knowledge graphs help AI systems understand complex relationships: which customers are connected to which accounts, which products relate to which components, which regulations apply to which processes. Combined with RAG, knowledge graphs enable AI to answer multi-hop questions like "Which of our clients in healthcare have contracts expiring this quarter?" by traversing entity relationships.

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Related Terms

RAG (Retrieval-Augmented Generation)

RAG is a technique that enhances AI responses by retrieving relevant information from external knowledge bases before generating an answer, combining the power of search with generative AI.

Semantic Search

Semantic search understands the meaning and intent behind queries rather than just matching keywords, using AI to deliver more relevant results from documents, databases, and knowledge bases.

Vector Database

A vector database is a specialized database optimized for storing and querying high-dimensional embeddings, enabling fast similarity search that powers RAG, recommendations, and semantic search.

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