neo4j
Understanding relationships ..
neo4j
Neo4j is a leading graph database management system designed to store, manage, and query highly connected data.
Unlike traditional relational databases that organize data in tables, Neo4j structures data as a graph of nodes, relationships, and properties.
Neo4j stores data in a property graph model where relationships are first-class citizens.
This approach makes traversing connections between data points extremely efficient - a significant advantage over relational databases that require expensive join operations.
Cypher Query Language
Neo4j uses Cypher, a declarative query language specifically designed for working with graph data.
Cypher syntax is visually intuitive, using ASCII art to represent patterns:
MATCH (person:Person)-[:LIVES_IN]->(city:City {name: "London"})
RETURN person.name
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Create the Neo4j directories.
cd
mkdir -p Neo4j/neo4j_db/{data,logs,import,plugins}Create a
docker-compose.ymlfile.
cd
cd Neo4j
nano docker-compose.ymlservices:
neo4j:
container_name: neo4j
image: neo4j:latest
ports:
- 7474:7474
- 7687:7687
environment:
- NEO4J_AUTH=neo4j/${NEO4J_PASSWORD}
- NEO4J_apoc_export_file_enabled=true
- NEO4J_apoc_import_file_enabled=true
- NEO4J_apoc_import_file_use__neo4j__config=true
- NEO4J_PLUGINS=["apoc", "graph-data-science"]
volumes:
- ./neo4j_db/data:/data
- ./neo4j_db/logs:/logs
- ./neo4j_db/import:/var/lib/neo4j/import
- ./neo4j_db/plugins:/pluginsCreate a file called
docker-compose.override.yml.
services:
neo4j:
environment:
- NEO4J_server_memory_heap_initial__size=6G
- NEO4J_server_memory_heap_max__size=6GAgain, you can create a
.env.exampleand gitignoring.env
cd
cd Neo4j
nano .envNEO4J_PASSWORD=<your password>Onnce everything is in place.
cd
cd Neo4j
docker-compose up -d
Log into Neo4j:
Username: neo4j
Password: password
Graph Builder
Neo4j Graph Builder is a tool designed to help users create, visualize, and interact with graph databases in a more intuitive way. It provides a visual interface that reduces the need for complex Cypher queries, allowing users to model and populate graph databases through an accessible UI rather than purely through code.
Data modeling is a primary use case, enabling users to visually design schemas, create node labels, establish relationship types, and define properties. The tool makes it easy to validate data models before implementation and to modify existing models as requirements evolve.
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Movie Graph
The database contains information about movies and person (actors). Additionally, it contains ratings about movies by users.
To get the basics of creating a Knowledge Graph - try out the Movie Graph tutorial.
Create the data - Cypher CREATE clause is used to create data.
Explore .. click on the various Node & Relationships perspectives .
Scroll down and run the Cypher query to constrain the dataset to Tom Hanks films and roles.
To improve Query performance, add indexes - movie_title & person_name.
Create a couple of indexes - person_born & movie_released.
Run some Cypher queries against the Nodes (Subjects) : Movie or Person (constraint)
Turning to Relationships (Predicate) - Match the pattern : ACTED_IN
There's an error in the query:
MATCH (p:Person {name: "Tom Hanks"})-[:ACTED_IN]->(movie) RETURN p.movie
The issue is the query is trying to return movie as a property key of Person when its a Node:
MATCH (p:Person {name: "Tom Hanks"})-[:ACTED_IN]->(movie) RETURN movie
Six degrees .. Traversing the Nodes
Cypher has a built-in algorithm - "shortestPath" - to determine the degree of separation between Nodes:
MATCH path=shortestPath( (:Person {name:"Kevin Bacon"})-[*]-(:Person {name:"Meg Ryan"}) ) RETURN path, length(path) / 2 as distance
Run queries that illustrate relationships, using shortestPath, in the dataset.
Recommendations
At the core of knowledge graph recommendations is the concept of graph traversal. When a user interacts with an item (like watching a movie or purchasing a product), the system can navigate through the knowledge graph to find related entities through various relationship pathways. These pathways often reveal non-obvious connections that purely statistical approaches might miss.
For example, in a movie recommendation system, a knowledge graph might connect films not just by genre or director, but through more nuanced relationships like "inspired by," "shares screenplay writer with," or "features similar thematic elements." This allows for recommendations that capture deeper similarities than surface-level attributes.
Run queries that list actors that have worked with his co-actors .. this can then be refined to a list actors that have worked with both actors.
To reset the database:
Run a schema reset query like:
CALL db.clearQueryCaches(); CALL apoc.schema.assert({},{},true); (if APOC is installed)
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