EAV-to-Graph Mapping

EAV-to-Graph Mapping is a process that transforms Entity-Attribute-Value triplets into a knowledge graph structure in Neo4j or other graph databases. Entity becomes an Entity-type node, Attribute becomes an Attribute-type node (or edge, depending on approach), and Value becomes a node property or separate node.

The recommended approach treats both Entity and Attribute as nodes connected by HAS_ATTRIBUTE edges; this enables SHARES_ATTRIBUTE analysis between entities that share the same attribute. EAV-to-graph mapping is the moment when static triplet tables become dynamic networks of connections with traversal, visualization, and graph metrics analysis capabilities.

For example, the triplet 'Cortisol | daily_peak | morning' in the graph becomes two nodes (Cortisol, daily_peak) and a HAS_ATTRIBUTE edge with property value='morning'. In practice, perform the mapping in Python: generate Cypher MERGE queries and batch-load them into Neo4j — manually creating nodes in the browser is inefficient for more than 20 triplets.