semantica.ontology provides the full lifecycle for knowledge graph schemas:
  • Auto-generate ontologies from KG data via a 5-stage pipeline (Semantic Network → YAML → Types → Hierarchy → TTL)
  • LLM-powered ontology generation for complex domains via LLMOntologyGenerator
  • SHACL validation: generate shapes, validate graphs, and get violation reports
  • OWL/RDF export in Turtle, RDF/XML, and JSON-LD formats
  • Ontology Hub visual editor available in semantica.explorer (v0.5.0)

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Exported Classes

ClassRole
OntologyEngineUnified facade orchestrating the full ontology lifecycle
OntologyGeneratorAuto-generate ontologies from KG data (5-stage pipeline)
LLMOntologyGeneratorLLM-powered ontology generation for complex domains
SHACLGeneratorGenerate SHACL shapes from an ontology or KG schema
OntologyValidatorValidate any graph against SHACL shapes: returns SHACLValidationReport
OWLGeneratorSerialize ontologies to Turtle, RDF/XML, JSON-LD
NamespaceManagerIRI generation, prefix management, and namespace binding
OntologyEvaluatorCoverage, completeness, and granularity quality metrics
ClassInferrerInfer classes from entity type patterns
PropertyGeneratorGenerate properties from entity attributes and relationships
AssociativeClassBuilderModel N-ary relationships as intermediate OWL classes

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Getting Started

OntologyEngine is your main entry point for the complete ontology workflow: 
from semantica.ontology import OntologyEngine

# Initialize with base URI for your domain
engine = OntologyEngine(base_uri="https://example.org/ontology/")

# Generate ontology from your knowledge graph data
ontology = engine.from_data({"entities": entities, "relationships": relationships})

# Validate a graph against the generated SHACL shapes
report = engine.validate_graph(kg, ontology=ontology)
if not report.conforms:
    for v in report.violations:
        print(f"{v.severity}: {v.message} on {v.focus_node}")

# Export to OWL Turtle
engine.export_owl(ontology, "ontology.ttl", format="turtle")

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OntologyEngine (Unified Facade)

OntologyEngine orchestrates the full ontology lifecycle: generation, validation, export, and evaluation: 
from semantica.ontology import OntologyEngine

engine = OntologyEngine(base_uri="https://example.org/ontology/")

# Generate ontology from KG data
ontology = engine.from_data({"entities": entities, "relationships": relationships})

# Validate a graph against the generated SHACL shapes
report = engine.validate_graph(kg, ontology=ontology)
if not report.conforms:
    for v in report.violations:
        print(f"{v.severity}: {v.message} on {v.focus_node}")

# Export to OWL Turtle
engine.export_owl(ontology, "ontology.ttl", format="turtle")

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OntologyEngine Methods

MethodDescription
from_data(data)Run the 5-stage pipeline on entity/relationship data
validate_graph(kg, ontology=...)Check a knowledge graph against generated SHACL shapes
export_owl(ontology, path, format)Serialize to "turtle", "xml", or "json-ld"
evaluate(ontology, kg)Compute coverage, completeness, and granularity metrics

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OntologyGenerator (5-Stage Pipeline)

OntologyGenerator auto-generates a formal ontology from your knowledge graph entities and relationships: 
from semantica.ontology import OntologyGenerator

generator = OntologyGenerator(base_uri="https://example.org/ontology/")
ontology  = generator.generate_ontology({
    "entities":      entities,
    "relationships": relationships,
})
1

Semantic Network Parsing

Extract concepts and patterns from entity types and relationship structures in the source data.
2

YAML-to-Definition

Transform the extracted patterns into intermediate class and property definitions.
3

Definition-to-Types

Map definitions to OWL constructs: owl:Class, owl:ObjectProperty, owl:DatatypeProperty.
4

Hierarchy Generation

Build taxonomy trees using transitive closure and cycle detection: produces rdfs:subClassOf chains.
5

TTL Generation

Serialize the final ontology to Turtle format using rdflib. Also available: RDF/XML and JSON-LD.

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SHACL Validation

Generate SHACL shapes from an ontology and validate any graph against them: 
from semantica.ontology import SHACLGenerator, OntologyValidator, SHACLValidationReport, SHACLViolation

# Generate shapes from ontology
generator  = SHACLGenerator()
shapes     = generator.generate(ontology)
shapes_ttl = shapes.serialize(format="turtle")

# Validate a graph against the shapes
validator = OntologyValidator()
report: SHACLValidationReport = validator.validate_graph(kg, ontology=ontology)

if not report.conforms:
    violation: SHACLViolation
    for violation in report.violations:
        print(f"{violation.severity}: {violation.message}")
        print(f"  Node: {violation.focus_node}")
        print(f"  Path: {violation.result_path}")

​
Validation Report Fields

FieldTypeDescription
conformsboolTrue if the graph passes all SHACL constraints
violationsList[SHACLViolation]Detailed failure records
focus_nodestrIRI of the violating graph node
result_pathstrIRI of the violating property path
severitystr"Violation", "Warning", or "Info"
messagestrHuman-readable constraint failure description

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LLM-Powered Ontology Generation

For complex or novel domains where schema patterns are hard to infer statistically: 
from semantica.ontology import LLMOntologyGenerator

# Initialize with your preferred LLM provider
generator = LLMOntologyGenerator(provider="openai")  # or "anthropic", "groq", etc.
ontology  = generator.generate_ontology_from_text(
    text="A biomedical ontology for clinical trial protocols involving patients, trials, interventions, and outcomes."
)

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OWL / RDF Export

from semantica.ontology import OWLGenerator

generator = OWLGenerator()
generator.export_owl(ontology, path="ontology.ttl",  format="turtle")
generator.export_owl(ontology, path="ontology.owl",  format="xml")
generator.export_owl(ontology, path="ontology.json", format="json-ld")

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Namespace Management

from semantica.ontology import NamespaceManager

ns = NamespaceManager(base_uri="https://example.org/")
ns.register("ex",     "https://example.org/")
ns.register("schema", "https://schema.org/")
ns.register("owl",    "http://www.w3.org/2002/07/owl#")

# Generate IRIs for classes and properties
class_iri    = ns.generate_class_iri("Person")
property_iri = ns.generate_property_iri("worksFor")

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Ontology Evaluation

Measure coverage, completeness, and granularity of a generated ontology: 
from semantica.ontology import OntologyEvaluator

evaluator = OntologyEvaluator()
result    = evaluator.evaluate_ontology(ontology, kg)

print(f"Class coverage:    {result.class_coverage:.2f}")
print(f"Property coverage: {result.property_coverage:.2f}")
print(f"Completeness:      {result.completeness:.2f}")
print(f"Granularity:       {result.granularity:.2f}")

for gap in result.gaps:
    print(f"Gap: {gap.description}")

​
Common Workflows

Generate and validate an ontology in 3 steps:
from semantica.ontology import OntologyEngine

# 1. Initialize engine
engine = OntologyEngine(base_uri="https://yourcompany.com/ontology/")

# 2. Generate from your data
ontology = engine.from_data({"entities": entities, "relationships": relationships})

# 3. Validate against a knowledge graph
report = engine.validate_graph(kg, ontology=ontology)
if report.conforms:
    print("✓ Graph conforms to ontology")
else:
    print(f"✗ Found {len(report.violations)} violations")

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Ingest an Existing Ontology

Load and parse an ontology file for downstream use: 
from semantica.ontology import ingest_ontology

ontology_data = ingest_ontology("schema.ttl")     # Turtle
ontology_data = ingest_ontology("schema.owl")     # OWL/XML
ontology_data = ingest_ontology("schema.jsonld")  # JSON-LD
Ontology versioning (VersionManager, OntologyVersion) has moved to semantica.change_management. Import from there: from semantica.change_management import VersionManager.

Reasoning

Apply inference rules over ontology axioms.

Knowledge Graph

The graph being modeled by the ontology.

Export

Export ontologies as RDF, OWL, or JSON-LD.

Conflicts

Detect ontology constraint violations.