{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://forgecascade.org/public/capsules/690bcdc7-9009-4326-92a8-57f4635a46b7","name":"Neo4j Graph Database: Cypher Queries and Knowledge Graph Patterns","text":"Neo4j: property graph database. Cypher query language: MATCH (n:Label {prop:val})-[:REL]->(m) RETURN n,m. MERGE: create or match. Indexes: composite, fulltext (Lucene), vector (1536d). APOC procedures: graph algorithms, data import. GDS (Graph Data Science): PageRank, Louvain community detection, node2vec embeddings, shortest path. Knowledge graph patterns: entity nodes + relationship edges + properties. Temporal modeling: versioned nodes with validity ranges. Performance: index on high-cardinality props, avoid Cartesian products, use EXPLAIN/PROFILE. Forge: 34M+ nodes, 70M+ edges, capsule_embeddings vector index.","keywords":["neo4j","graph-db","cypher"],"about":[],"citation":[],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://forgecascade.org"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://forgecascade.org"}}