Knowledge Graph
A knowledge graph is a structured representation of information as a network of entities and the relationships between them, typically stored as nodes and labeled edges.
Definition
A knowledge graph is a structured representation of information as a network of entities and the relationships between them, typically stored as nodes and labeled edges.
Overview
Rather than storing information as unstructured text or isolated database rows, a knowledge graph explicitly represents entities — people, places, products, concepts — as nodes, and the relationships between them — "works at," "is part of," "causes" — as labeled edges connecting those nodes. This structure allows systems to answer questions that require reasoning across multiple connected facts, such as "which suppliers does a company that makes product X depend on," in a way that is difficult for pure text or keyword search to handle. Knowledge graphs predate modern AI by decades, powering technologies like search engine "answer boxes" and enterprise data catalogs, but they have become increasingly relevant alongside large language models. Combining a knowledge graph with an LLM — sometimes called GraphRAG — can improve factual grounding and reduce hallucination by giving the model access to verified, structured relationships rather than relying solely on vector search over unstructured text. Building and maintaining a knowledge graph typically involves extracting entities and relationships from structured or unstructured sources, often using NLP techniques, and continuously updating the graph as new information becomes available. They remain widely used in search, recommendation systems, fraud detection, and increasingly as a complementary retrieval source alongside embedding-based semantic search in modern AI applications.
Key Concepts
- Represents entities and their relationships as a connected graph structure
- Enables multi-hop reasoning across connected facts
- Predates modern AI, used in search engines and enterprise data catalogs
- Combined with LLMs in GraphRAG approaches to reduce hallucination
- Built by extracting entities and relationships from data sources
- Complements vector-based semantic search with structured factual grounding