AI Glossaryinfrastructure

Knowledge Graph

A structured representation of information as a network of entities and their relationships, used to give AI systems organized, queryable world knowledge.

How It Works

Knowledge graphs store information as triples: (entity, relationship, entity). For example: (Paris, capital_of, France), (France, continent, Europe), (Eiffel Tower, located_in, Paris). This structure makes it easy to answer questions by traversing relationships: "What continent is the city with the Eiffel Tower in?" → Eiffel Tower → Paris → France → Europe. In AI applications, knowledge graphs complement LLMs by providing structured, verifiable facts. While an LLM might hallucinate that the Eiffel Tower is in London, a knowledge graph query returns the correct answer deterministically. The combination is powerful: use the LLM to understand the question and generate natural language, and use the knowledge graph for factual grounding. Building knowledge graphs: (1) Extract entities and relationships from documents using NER and relation extraction. (2) Store in a graph database (Neo4j, Amazon Neptune, or even a relational database with join tables). (3) Query using graph traversal or SPARQL. (4) Keep updated through automated extraction pipelines. For AI applications, knowledge graphs are especially valuable for domain-specific systems (medical, legal, financial) where accuracy matters more than general knowledge.

Common Use Cases

  • 1Enterprise knowledge management
  • 2Medical and scientific research
  • 3Recommendation engines
  • 4Search engine enhancement
  • 5Fraud detection through relationship analysis

Related Terms

RAG (Retrieval-Augmented Generation)

A technique that enhances AI responses by retrieving relevant information from a knowledge base before generating an answer.

Semantic Search

A search approach that finds results based on meaning rather than exact keyword matches, using embeddings to understand the intent behind queries.

Named Entity Recognition (NER)

An NLP technique that identifies and classifies named entities in text, such as people, organizations, locations, dates, and monetary values.

Grounding (AI)

Connecting AI model outputs to verifiable sources of truth — such as retrieved documents, databases, or real-time data — to reduce hallucination and increase factual accuracy.

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