Knowledge Graph

A Knowledge Graph is a data structure composed of nodes (entities/attributes) and edges (relationships between them), representing knowledge about a topic in a way that both humans and machines can understand. Google has been building its own Knowledge Graph since 2012, and in semantic SEO we create local knowledge graphs for client websites, mapping the EAV model onto a graph structure: Entity = node, Attribute = node, Value = node, with HAS_ATTRIBUTE relationships connecting entities to attributes.

Knowledge graphs provide three advantages over embeddings alone: explainability (you can see WHY something is related), hierarchy (layers of Core Unique → Strong Direct → Relevant Contextual), and directionality (relationships have labels and strength).

Knowledge graphs are built in Neo4j using Cypher language and expanded iteratively via MERGE operations. In practice, start with a small graph (1 central entity + 10-15 attributes) and expand gradually — attempting to build a complete graph from the start ends in chaos.