Knowledge Graph

A knowledge graph is a database that stores information as a network of entities and relationships. Unlike traditional databases that organise data in rows and columns, knowledge graphs represent facts as triples: subject-predicate-object statements like "London is-capital-of United Kingdom" or "Aspirin treats Headache." This structure mirrors how information is naturally interconnected. By following relationships, a knowledge graph can answer complex queries that would require multiple joins in a relational database — for example, "Which medications interact with drugs prescribed for conditions that run in this patient's family?"

Knowledge graphs are typically built on graph database technologies like Neo4j, Amazon Neptune, or open standards like RDF and SPARQL. Entities (nodes) represent things — people, products, concepts, locations — and relationships (edges) represent how those things connect. Building a knowledge graph involves entity extraction (identifying entities in unstructured data), relationship extraction (determining how entities relate), entity resolution (ensuring the same real-world entity is represented consistently), and ontology design (defining the types of entities and relationships the graph supports). AI is increasingly used both to build knowledge graphs (using NLP to extract entities and relationships from text) and to query them (using natural language interfaces that translate questions into graph queries). This creates a virtuous cycle where AI improves the knowledge graph, and the knowledge graph improves AI accuracy.

Knowledge graphs complement vector databases and RAG systems by providing structured, relational context that embeddings alone cannot capture. While embeddings excel at semantic similarity, knowledge graphs excel at capturing explicit relationships — hierarchies, causation, temporal sequences, and constraints. For enterprises, knowledge graphs unify information scattered across multiple systems into a coherent, queryable structure. A pharmaceutical company can connect drugs, conditions, clinical trials, and regulatory approvals. A financial institution can map relationships between entities for compliance and risk assessment. The combination of knowledge graphs with LLMs — sometimes called GraphRAG — produces more accurate and contextually rich AI responses than either technology alone, particularly for queries that require understanding relationships between entities.

Knowledge graphs are widely used in healthcare (connecting symptoms, diagnoses, treatments, and outcomes), finance (mapping corporate structures and transaction networks for fraud detection), e-commerce (powering product recommendations based on attribute relationships), and search engines (Google's Knowledge Graph powers rich search results). In enterprise AI, knowledge graphs enhance RAG systems by providing structured context alongside document retrieval. They enable more precise question answering, better entity disambiguation, and richer explanations of how conclusions were reached.