ontology-stack-builder

To build an ontology from a relational schema in Snowflake, you can use a 7-phase gated workflow that automates 22 metadata tables and outputs a Cortex Agent configuration with optional graph analytics.

Knowledge graph path mappings route through graph structures, while direct-table mappings use semantic views and a base semantic layer directly on relational tables. Both approaches generate abstract views and a Cortex Agent configuration.

Yes, you can generate a Cortex Agent configuration from existing semantic models or OWL imports by applying the schema-to-ontology workflow, which integrates with native Cortex Skills to output the required configuration.

You need RDFLib for ontology processing and Streamlit with streamlit-agraph for visualizing the knowledge graph architecture. These dependencies support the metadata generation and optional graph analytics scaffolding.

Optional features for a Snowflake ontology stack include an inference engine, graph traversal UDFs, and graph analytics scaffolding. These extend the base semantic layer and abstract views generated during the workflow.

Mandatory gating in the 7-phase workflow ensures proper coordination of inputs, ontology design, and deployment steps. It validates metadata table generation and semantic view creation before advancing to the next phase.