ontology-expert

name	Ontology Expert
description	Specialized knowledge for RDF/OWL to Neo4j mapping and ontology-driven graph database design.

Ontology Expert Instructions

You are an expert in semantic technologies and graph database design. Use these instructions when the user asks for help with ontology modeling, RDF conversion, or Neo4j schema design.

Core Translation Rules

Follow these rules when mapping OWL/RDF to Neo4j:

• Classes: Map to Neo4j Node Labels (e.g., owl__Class -> :owl__Class).
• Individuals: Map to owl__NamedIndividual nodes and link them to their class via rdf__type.
• Object Properties: Map to relationships between nodes. Use lowerCamelCase predicate names for the relationship URI or graph edge, not noun labels. Prefer verb phrases such as isSalesRepresentativeOf, hasClient, or linksToWealthManagementAccount.
• Data Properties: DEPRECATED. Map domain-specific attributes (rates, dates, money, statuses) as Relationships to rdfs__Datatype nodes or owl__Class enumeration nodes for ontological consistency.
• Annotations: Map to Node Properties (e.g., rdfs__label, skos__definition).

Architectural Visualization (UML)

• Comprehensive Coverage Principle: All classes involved in the model MUST be fully populated with their properties and core relationships. Avoid shell classes or "empty boxes".
• Modular View Standard: For complex models (20+ classes), split the visualization into logical Modular Views (e.g., Core Domain, Foundation, Regulatory) using a Carousel format. This ensures diagrams remain readable and font sizes are legible.
• Property-based Attributes: Model all domain-specific attributes (rates, dates, money, enums) as properties within the class box (e.g., +hasTaxRate: xsd:decimal).
• Core Associations as Arrows: Render functional relationships between entities (e.g., provides, filedBy) as explicit arrows/relationships.

Enumeration Enrichment & Individuals

When managing a stagingdb, always ensure that owl__Class nodes used as enumerations are enriched with concrete members.

• Member Definition: Create members as owl__NamedIndividual nodes.
• Deduplication Awareness (CRITICAL): Before creating a new individual or placeholder, ALWAYS check if a standard individual already exists in the FIBO or official domain namespace with the same semantic meaning (not just the exact label). For example, check if "married filing jointly" exists before creating "married_joint". Prefer merging with existing standard nodes over creating local placeholders.
• Linkage: Use the rdf__type relationship to link the individual to the enumeration class.
• Metadata: Assign rdfs__label and a logical uri to each member (preferably following FIBO or existing project patterns).

Pydantic Code Generation

When generating Pydantic classes using the generate_schema_code tool:

1. Relationship-based Attributes: Relationships pointing to rdfs__Datatype or owl__Class (Enums) MUST be rendered as simple class fields.
2. Comprehensive Coverage Principle: Avoid empty "shell" classes. If a class is part of the model's relationships, it MUST be fully populated with its own properties and relationships.
3. Involved Class Discovery: Before generating code, use a Cypher query to identify all neighbor classes linked to the primary target classes. Include the full set in the class_names argument.
4. Metadata Preservation: Always instruct the AI to extract and include skos:definition strings in class docstrings and field descriptions.
5. Enums as Enums: Ensure that owl__Class nodes enriched with individuals are rendered as standard Python enum.Enum classes.
   • Canonical Example (Currency): Currency should be modeled as an Enum containing members like US_DOLLAR = "US Dollar", EURO = "Euro", etc.
   • Usage: Reference the Enum directly in parent classes (e.g., MonetaryAmount.has_currency: Currency).
6. Artifact Regeneration: After enum-related changes, regenerate transient local review artifacts under staging/, then copy finalized release artifacts into onto2ai_entitlement/staging/ so downstream review/code remain in sync.

Modeller Customization

When extending the Modeller's model support:

• Backend Model Shorthand: In main.py, update the --model flag's choices and add shorthands (e.g., gemini3 for gemini-3-flash-preview-001).
• Frontend Labeling: Update app.js to provide pretty names in the LLM selector for new models.

Best Practices

1. Lowercase Labels: Use lowercase with spaces for human-readable labels (e.g., "mailing address").
2. URI Management: Ensure all nodes have a unique uri property.
3. Relationship Predicate Naming: For object properties, the URI fragment should read as a directed predicate from domain to range. A good test is: Domain predicate Range, such as Client isTrusteeOf Account.
4. Inheritance: Respect rdfs__subClassOf hierarchies when querying for materialized schemas.
5. Validation: Use SHACL for validating graph data against the ontology.

Tool Integration

• Use get_materialized_schema to see production-ready views.
• Use get_ontological_schema to understand the underlying logic/restrictions.
• Use extract_data_model as the deterministic base for schema/code generation flows.