Knowledge Graph
Quick Answer
A knowledge graph is a structured representation of entities (people, products, places, concepts) and the relationships between them. Knowledge graphs give AI systems explicit, queryable context that complements the implicit knowledge inside language models.
In Depth
What Knowledge Graph really means
Nodes represent entities; edges represent relationships; properties describe attributes. Knowledge graphs can be derived from structured enterprise data, extracted from unstructured documents, or curated by experts, usually a blend of all three.
Combining a knowledge graph with an LLM — sometimes called GraphRAG — yields more precise, explainable and controllable outputs than either approach alone, particularly in regulated or factually sensitive domains.
Why It Matters
Business relevance for UK organisations
UK organisations use knowledge graphs to unify fragmented data across CRM, ERP, product systems and external sources, creating a shared semantic layer that makes downstream AI applications more reliable.
Real-world example
How this shows up in practice
A Manchester pharmaceutical company built a knowledge graph linking trials, molecules, papers and researchers, enabling natural-language queries that previously required 3–4 specialists to answer.
Related Terms
Continue exploring
RAG (Retrieval Augmented Generation)
Retrieval Augmented Generation (RAG) is an architecture that combines a language model with an external knowledge source. Before generating an answer, the system retrieves relevant documents and feeds them to the model as context, dramatically reducing hallucinations and keeping answers current.
TechnicalEmbedding
An embedding is a numerical vector representation of text, images or other data that captures semantic meaning. Items with similar meaning produce similar vectors, which makes embeddings the backbone of semantic search, recommendations and RAG systems.
AdvancedNatural Language Processing (NLP)
Natural Language Processing is the field of AI concerned with interpreting, understanding and generating human language. NLP underpins chatbots, translation, summarisation, sentiment analysis, voice assistants and much of the productivity software UK teams now rely on daily.
AdvancedExplainable AI
Explainable AI (XAI) is a set of techniques and design practices that make AI systems' decisions understandable to humans. Explainability supports trust, debugging, accountability and regulatory compliance, particularly in consequential decisions.