Knowledge Graph - Graph Constraint Generator

Generate structural, semantic, and property constraints for knowledge graph schemas including RDF/OWL ontologies and property graph models.

Knowledge Graph Constraint Generator

Generate constraints for knowledge graph schemas to enforce data integrity.

This skill automatically creates structural, semantic, and property constraints suitable for RDF/OWL ontologies and Neo4j property graphs.

Quick Start

Use When

• Designing constraints for graph schemas
• Enforcing graph integrity rules
• Generating SHACL validation rules
• Creating Neo4j database constraints
• Defining property requirements
• Establishing data governance

Inputs

• Graph schema definitions
• Entity models
• Relationship types
• Property specifications
• Cardinality requirements
• Integrity rules

Outputs

• Unique constraints
• Required property constraints
• Relationship constraints
• Cardinality rules
• SHACL shapes
• Cypher constraint statements

Example

Input Schema:

Student: student_id (unique), name (required), email
Course: course_code (unique), title (required), credits
Relationship: (Student)-[:ENROLLED_IN]->(Course)

Generated Constraints:

Unique:
  Student.student_id UNIQUE
  Course.course_code UNIQUE

Required:
  Student.name (min 1)
  Course.title (min 1)

Relationships:
  (Student)-[:ENROLLED_IN]->(Course) [min 1]

Constraint Types

1. Unique Constraints

Ensure properties uniquely identify nodes

CREATE CONSTRAINT student_id_unique ON (s:Student) REQUIRE s.student_id IS UNIQUE

2. Required Property Constraints

Define mandatory properties

Student.name IS REQUIRED
Course.title IS REQUIRED

3. Relationship Constraints

Define allowed graph connections

(Student)-[:ENROLLED_IN]->(Course)
(Professor)-[:TEACHES]->(Course)

4. Cardinality Constraints

Define relationship occurrence limits

Student → ENROLLED_IN → Course (minimum: 1)
Professor → TEACHES → Course (maximum: 10)

5. Domain/Range Constraints (RDF/OWL)

Define semantic relationship constraints

:writes rdfs:domain :Researcher ;
        rdfs:range :Paper .

6. Data Type Constraints

Ensure property values match types

student_id: String
credits: Integer
gpa: Float

Execution Steps

1. Analyze Schema – Extract entities, relationships, properties
2. Identify Keys – Find unique identifiers
3. Determine Required – Identify mandatory properties
4. Analyze Relationships – Extract connection patterns
5. Calculate Cardinality – Determine relationship bounds
6. Generate Constraints – Create constraint definitions
7. Format Output – Output as SHACL, Cypher, or RDF

Output Formats

Neo4j Cypher

CREATE CONSTRAINT student_id_unique ON (s:Student) REQUIRE s.student_id IS UNIQUE
CREATE INDEX ON (s:Student)(name)

SHACL Shapes

:StudentShape a sh:NodeShape ;
  sh:targetClass :Student ;
  sh:property [
    sh:path :student_id ;
    sh:minCount 1 ;
    sh:maxCount 1
  ] .

RDF/OWL

:writes rdfs:domain :Researcher ;
        rdfs:range :Paper ;
        rdf:type owl:ObjectProperty .

Recommended Libraries

• RDF/OWL: rdflib, pyshacl, owlready2
• Property Graph: neo4j, py2neo, networkx
• Data Validation: pydantic, jsonschema

Best Practices

✓ Define unique identifiers early
✓ Avoid overly restrictive constraints
✓ Keep domain/range rules clear
✓ Validate data frequently
✓ Combine database and semantic constraints
✓ Document constraint reasoning
✓ Test constraints before production

References

See constraint-patterns.md for constraint design patterns and example-constraints.md for domain constraint examples.

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Version: 1.0.0