What Is a Knowledge Graph?
A knowledge graph is a structured network that encodes information as nodes (entities), edges (relationships between them), and properties or attributes, forming a semantic network machines can traverse rather than a flat table of records (Wikipedia, Knowledge graph, 2025). This structure lets software reason about how things connect, not just what they are.
How Does a Knowledge Graph Work?
A knowledge graph represents facts as triples: subject, predicate, object. For example: "Python" connects to "programming language" via the relationship "is a". Each node can carry properties such as version, creator, or license, and each edge defines the direction and type of the connection. The result is a machine-readable web of meaning that can answer questions requiring multiple hops across related facts.
This structure is what makes graph-augmented retrieval useful for AI systems. GraphRAG, introduced by Microsoft Research in early 2024, layers a knowledge graph's entity-and-relationship structure on top of vector retrieval so an LLM can answer multi-hop questions about how concepts connect, not just retrieve isolated similar chunks (Retrieval-Augmented Generation with Graphs (GraphRAG), arXiv survey, 2025). Without the graph layer, a retrieval system might surface relevant passages but miss that Company A acquired Company B, which explains why both share the same API dependency.
Use Cases
AI grounding and GraphRAG. LLMs produce fewer incorrect answers when responses are grounded in a structured factual source. A knowledge graph acts as a backbone, connecting entities an LLM might conflate or invent. Teams building internal AI assistants wire a domain knowledge graph into their retrieval pipeline so the model can traverse relationships rather than match keywords alone.
Enterprise search and data integration. Large organizations manage data spread across CRMs, databases, and documents. A knowledge graph unifies those sources into a single semantic layer, letting complex queries return correct results even when the underlying data lives in separate systems.
Web data pipelines for knowledge construction. Building a knowledge graph requires structured input: entity names, types, and relationships extracted from raw text or HTML. Web scraping and structured data extraction feed this process. APIs that return pages as clean HTML or Markdown reduce the preprocessing work needed before entity extraction can begin, which is one practical reason teams reach for a rendering layer like Massive's Web Render API when sourcing web data for graph construction.
Frequently Asked Questions
A relational database stores data in rows and columns with a fixed schema. A knowledge graph stores data as nodes and edges, which makes it natural to model arbitrary relationships between entities and traverse multi-hop connections without expensive join operations.
GraphRAG is a retrieval technique, introduced by Microsoft Research in early 2024, that combines vector search with a knowledge graph so an LLM can answer questions about how entities relate to each other, not just which documents are semantically similar (Retrieval-Augmented Generation with Graphs (GraphRAG), arXiv survey, 2025).
Knowledge graphs are built by extracting entities and relationships from structured or unstructured sources, including web pages, documents, and APIs. Tools that return clean, parseable content in HTML or Markdown format reduce the preprocessing work required before entity extraction can begin.
No. A vector database stores numerical embeddings and retrieves results by similarity score. A knowledge graph stores named entities and explicit, typed relationships and retrieves by traversal. The two are complementary: GraphRAG combines both approaches to handle queries that need semantic similarity and structural reasoning at the same time.