semantica/docs/reference/ontology.md

Ontology

Automated ontology generation, validation, and management system.

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🎯 Overview

• :material-factory:{ .lg .middle } Automated Generation

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  6-stage pipeline to generate OWL ontologies from raw data

• :material-sitemap:{ .lg .middle } Inference Engine

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  Infer classes, properties, and hierarchies from entity patterns

• :material-check-decagram:{ .lg .middle } Validation

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  Symbolic reasoning (HermiT/Pellet) for consistency checking

• :material-recycle:{ .lg .middle } Reuse Management

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  Import and align with standard ontologies (FOAF, Schema.org)

• :material-chart-bar:{ .lg .middle } Evaluation

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  Assess ontology quality using coverage, completeness, and granularity metrics

• :material-file-code:{ .lg .middle } OWL/RDF Export

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  Export to Turtle, RDF/XML, and JSON-LD formats

!!! tip "When to Use" - Schema Design: When defining the structure of your Knowledge Graph - Data Modeling: To formalize domain concepts and relationships - Interoperability: To ensure your data follows standard semantic web practices - Validation: To enforce constraints on your data

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⚙️ Algorithms Used

6-Stage Generation Pipeline

1. Semantic Network Parsing: Extract concepts and patterns from raw entity/relationship data.
2. YAML-to-Definition: Transform patterns into intermediate class definitions.
3. Definition-to-Types: Map definitions to OWL types (owl:Class, owl:ObjectProperty).
4. Hierarchy Generation: Build taxonomy trees using transitive closure and cycle detection.
5. TTL Generation: Serialize to Turtle format using rdflib.
6. Symbolic Validation: Run reasoner to check for logical inconsistencies.

Inference Algorithms

• Class Inference: Clustering entities by type and attribute similarity.
• Property Inference: Determining domain/range based on connected entity types.
• Hierarchy Inference: A is_a B detection based on subset relationships.

Validation

• Symbolic Reasoning: Uses HermiT or Pellet to check satisfiability.
• Constraint Checking: Validates cardinality, domain, and range constraints.
• Hallucination Detection: LLM-based verification of generated concepts.

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Main Classes

OntologyGenerator

Main entry point for the generation pipeline.

Methods:

Method	Description
generate_ontology(data)	Run full pipeline
generate_from_schema(schema)	Generate from explicit schema

Example:

from semantica.ontology import OntologyGenerator

generator = OntologyGenerator(base_uri="http://example.org/onto/")
ontology = generator.generate_ontology({
    "entities": entities,
    "relationships": relationships
})
print(ontology.serialize(format="turtle"))

OntologyValidator

Validates ontology consistency.

Methods:

Method	Description
validate(ontology)	Run symbolic reasoner
check_constraints(ontology)	Check structural rules

OntologyEvaluator

Scores ontology quality.

Methods:

Method	Description
evaluate(ontology)	Calculate all metrics
check_competency(questions)	Verify coverage

ReuseManager

Manages external dependencies.

Methods:

Method	Description
import_ontology(uri)	Load external ontology
align_concepts(source, target)	Map equivalent classes

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Convenience Functions

from semantica.ontology import generate_ontology, validate_ontology

# Quick generation
onto = generate_ontology(data, method="default")

# Quick validation
is_valid, report = validate_ontology(onto)

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Configuration

Environment Variables

export ONTOLOGY_BASE_URI="http://my-org.com/ontology/"
export ONTOLOGY_REASONER="hermit"
export ONTOLOGY_STRICT_MODE=true

YAML Configuration

ontology:
  base_uri: "http://example.org/"
  generation:
    min_class_size: 5
    infer_hierarchy: true
    
  validation:
    reasoner: hermit
    timeout: 60

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Integration Examples

Schema-First Knowledge Graph

from semantica.ontology import OntologyGenerator
from semantica.kg import KnowledgeGraph

# 1. Generate Ontology from Sample Data
generator = OntologyGenerator()
ontology = generator.generate_ontology(sample_data)

# 2. Initialize KG with Ontology
kg = KnowledgeGraph(schema=ontology)

# 3. Add Data (Validated against Ontology)
kg.add_entities(full_dataset)  # Will raise error if violates schema

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Best Practices

1. Reuse Standard Ontologies: Don't reinvent Person or Organization; import FOAF or Schema.org using ReuseManager.
2. Validate Early: Run validation during generation to catch logical errors before populating the graph.
3. Use Competency Questions: Define what questions your ontology should answer and use OntologyEvaluator to verify.
4. Version Control: Treat ontologies like code. Use VersionManager to track changes.

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See Also

• Knowledge Graph Module - The instance data following the ontology
• Reasoning Module - Uses the ontology for inference
• Visualization Module - Visualizing the class hierarchy