Property Graph Schema Designer#

Design Neo4j-style property graph schemas from domain descriptions.

This skill translates domain models, requirements, and entity descriptions into structured property graph schemas with node labels, relationships, properties, and constraints.

Quick Start#

Use When#

• Designing property graph schemas for Neo4j
• Converting domain documentation → graph models
• Creating graph database data models
• Modeling entities and relationships
• Bootstrapping graph projects

Inputs#

• Domain descriptions
• System architecture docs
• Entity relationship diagrams
• Relational schemas
• JSON/API schemas
• Business rules

Outputs#

• Node labels (e.g., Student, Course)
• Relationship types (e.g., ENROLLED_IN, TEACHES)
• Node properties with types
• Relationship properties
• Index recommendations
• Constraint suggestions
• Cypher implementation templates

Example#

Input:

text

A university has students, professors, courses, and departments.
Students enroll in courses. Professors teach courses.
Departments manage professors and courses.

Output:

text

Node Labels:
- Student (properties: student_id, name, email, enrollment_year)
- Professor (properties: professor_id, name, research_area)
- Course (properties: course_id, title, credits)
- Department (properties: dept_id, name, budget)

Relationships:
- (Student)-[:ENROLLED_IN {semester}]->(Course)
- (Professor)-[:TEACHES {semester}]->(Course)
- (Department)-[:MANAGES]->(Course)
- (Department)-[:EMPLOYS]->(Professor)

Constraints:
- CREATE CONSTRAINT student_id UNIQUE on Student.student_id
- CREATE CONSTRAINT course_id UNIQUE on Course.course_id
- CREATE INDEX on Professor.name

Execution Steps#

1. Identify Entities – Extract nouns/concepts from domain text
2. Map to Node Labels – Determine PascalCase labels
3. Identify Relationships – Extract verbs and connections
4. Map Relationship Types – Determine SCREAMING_SNAKE_CASE types
5. Extract Properties – Identify node & relationship attributes
6. Suggest Constraints – Recommend unique constraints
7. Suggest Indexes – Recommend indexes for queries
8. Generate Cypher – Output implementation templates

Schema Components#

Node Labels#

text

PascalCase naming
Examples: Student, Professor, Course, Department

Relationship Types#

text

SCREAMING_SNAKE_CASE naming
Directional: A-[TYPE]->B
Examples: ENROLLED_IN, TEACHES, MANAGES

Properties#

text

Node properties: camelCase (student_id, name, email)
Relationship properties: camelCase (semester, grade)
Include types: String, Integer, Date, Float

Constraints & Indexes#

text

Unique constraints on identifiers
Indexes on frequently queried properties
Recommended indexes for relationships

Recommended Libraries#

• Drivers: neo4j, py2neo
• Graph modeling: networkx
• Visualization: pyvis, graphviz
• Data: pandas

Best Practices#

✓ Use clear, consistent naming (PascalCase, SCREAMING_SNAKE_CASE, camelCase)

✓ Avoid redundant relationships

✓ Prefer directional relationships

✓ Use unique constraints on identifiers

✓ Index frequently queried properties

✓ Keep nodes focused, avoid over-nesting

✓ Separate schema concerns from instance data

✓ Plan for query patterns upfront

References#

See schema-patterns.md for property graph design patterns and example-schemas.md for domain schema examples.

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Version: 1.0.0