Knowledge Graph

A knowledge graph is a network of real-world entities (people, places, concepts, events) and the relationships between them. Data is stored as subject-predicate-object triples (e.g., "Toronto" → "is located in" → "Canada").

Knowledge graphs power Google's Knowledge Panel, Amazon's product recommendations, and enterprise search systems. They excel at answering questions that require connecting multiple facts across different sources.

In AI applications, knowledge graphs complement LLMs and RAG systems by providing structured, traversable relationships. While vector search finds semantically similar documents, graph traversal finds logically connected information through explicit relationships.

Building a knowledge graph involves entity extraction (identifying entities in text), relationship extraction (finding connections), entity resolution (merging duplicates), and graph storage (using Neo4j, Amazon Neptune, or similar graph databases).