concept-inversion/knowledge_graph

Knowledge_graph

Database version:

Neo4j 3.4.5

Query:

// Load data from csv db.csv with a single relationship

load csv with headers from "file:///db.csv" as csvoutline merge(word1:Word{name:csvoutline.source}) merge(word2:Word{name:csvoutline.destination}) merge (word1)-[:RELATED{weight:csvoutline.distance}]-(word2)

// load data with multople relationship and weight

load csv with headers from "file:///db.csv" as csvoutline merge(word1:Word{name:csvoutline.source}) merge(word2:Word{name:csvoutline.destination}) with word1, word2, csvoutline CALL apoc.create.relationship(word1, csvoutline.relationshipType,{weight:csvoutline.distance}, word2) yield rel Return count(word1)

// Query to match the words MATCH (n:Word)-[r]->(n2:Word) where n.name='fawn' RETURN n2,r.weight order by r.weight desc limit 5

// neighbor nodes and total distance to each query words match(n:Word)-[r]->(n1:Word)-[r2]->(n2:Word) where n1.name='pilot' AND n.name in ['captain','chair'] return n.name as source,n2.name as destination,sum(r.weight+r2.weight) as total order by destination,total

// Minumum Spanning Tree match (n:Word)-[r]->() where n.name in ['sofa','fawn'] call algo.spanningTree.minimum('Word','RELATED','weight',id(n),{write:true, writeProperty:"MINST"}) yield effectiveNodeCount return effectiveNodeCount;

// Read MST Relationship match path = (n:Word)-[:MINST]->() where n.name in ['sofa','fawn'] with relationships(path) as rels unwind rels as rel with distinct rel as rel Return startNode(rel).name as source, endNode(rel).name as destination, rel.weight as cost

// delete relationship MATCH p=()-[r:MINST]->() delete r