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-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

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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.

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.

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Main Classes

OntologyEngine

Unified orchestration for generation, inference, validation, OWL export, and evaluation.

Methods:

Method	Description
from_data(data, **options)	Generate ontology from structured data
from_text(text, provider=None, model=None, **options)	LLM-based generation from text
validate(ontology, **options)	Validate ontology consistency
infer_classes(entities, **options)	Infer classes from entities
infer_properties(entities, relationships, classes, **options)	Infer properties
evaluate(ontology, **options)	Evaluate ontology quality
to_owl(ontology, format="turtle")	Export OWL/RDF serialization
export_owl(ontology, path, format="turtle")	Save OWL to file

Quick Start:

from semantica.ontology import OntologyEngine

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

data = {"entities": entities, "relationships": relationships}
ontology = engine.from_data(data, name="MyOntology")

turtle = engine.to_owl(ontology, format="turtle")

LLMOntologyGenerator

LLM-based ontology generation with multi-provider support (openai, groq, deepseek, huggingface_llm).

Example:

from semantica.ontology import OntologyEngine

text = "Acme Corp. hired Alice in 2024. Alice works for Acme."
engine = OntologyEngine()

ontology = engine.from_text(
    text,
    provider="deepseek",
    model="deepseek-chat",
    name="EmploymentOntology",
    base_uri="https://example.org/employment/",
)

Environment variables:

export OPENAI_API_KEY=...
export GROQ_API_KEY=...
export DEEPSEEK_API_KEY=...

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"))

OntologyEvaluator

Scores ontology quality.

Methods:

Method	Description
evaluate_ontology(ontology)	Calculate evaluation metrics
calculate_coverage(ontology, questions)	Verify coverage

ReuseManager

Manages external dependencies.

Methods:

Method	Description
import_external_ontology(uri, ontology)	Load and merge external ontology
evaluate_alignment(uri, ontology)	Assess alignment and compatibility

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Unified Engine Examples

from semantica.ontology import OntologyEngine

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

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

# Validate
result = engine.validate(ontology, reasoner="hermit")
print("valid=", result.valid, "consistent=", result.consistent)

# Export
turtle = engine.to_owl(ontology, format="turtle")

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Configuration

Environment Variables

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

YAML Configuration

ontology:
  base_uri: "http://example.org/"
  generation:
    min_class_size: 5
    infer_hierarchy: true

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Integration Examples

Schema-First Knowledge Graph

from semantica.ontology import OntologyEngine
from semantica.kg import KnowledgeGraph

# 1. Generate Ontology from Sample Data
engine = OntologyEngine()
ontology = engine.from_data(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

Cookbook

• Ontology
• Unstructured to Ontology