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Ontology

Automated ontology generation, validation, and management system.

🎯 Overview

  • Automated Generation

    6-stage pipeline to generate OWL ontologies from raw data

  • Inference Engine

    Infer classes, properties, and hierarchies from entity patterns

  • Evaluation

    Assess ontology quality using coverage, completeness, and granularity metrics

  • OWL/RDF Export

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

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

⚙️ Algorithms Used

6-Stage Generation Pipeline

The ontology generation process follows these stages:

  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

The module uses several 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

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

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")

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

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

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.

See Also

Cookbook

Interactive tutorials to learn ontology generation and management:

  • Ontology: Define domain schemas and ontologies to structure your data
  • Topics: OWL, RDF, schema design, ontology generation
  • Difficulty: Intermediate

  • Use Cases: Structuring domain knowledge, schema definition

  • Unstructured to Ontology: Generate ontologies automatically from unstructured data

  • Topics: Automatic ontology generation, 6-stage pipeline, OWL validation
  • Difficulty: Advanced
  • Use Cases: Domain modeling, automatic schema generation