Semantic Data Engineer — Refactor Workflow

Description: Improve existing artifact structure and quality

When to Use

Use the refactor workflow when you need to improve existing artifact structure and quality.

Input Requirements

• Source data in relational, CSV, JSON, and unstructured formats
• Ontology schemas from Ontology Architect (OA)
• Entity resolution rules and link prediction models
• SPARQL query requirements and endpoint configurations

Process

1. Initialize — Set up the refactor context for Semantic Data Engineer
2. Execute — Perform the refactor operation following Semantic Data Engineer's style
3. Validate — Check output against quality gates
4. Handoff — Deliver results to downstream personas

Output

• RDF knowledge graph datasets conforming to target ontologies
• R2RML/RML mapping specifications for reproducible transformation
• Entity resolution reports with precision and recall metrics
• SPARQL endpoint documentation with query examples and performance benchmarks

Quality Gates

• All transformations must produce valid RDF conforming to target ontologies
• Entity resolution must achieve defined precision and recall thresholds
• SPARQL endpoints must meet query performance SLAs
• Data lineage must be tracked from source through transformation to graph