Raw SPARQL queries expose security risks, performance problems, and require too much schema knowledge. Storing queries as named, self-describing entities in RDF itself solves these issues by creating a controlled API layer that handles parameterization, access control, and logging.

• Direct SPARQL queries are dangerous: simple queries can crash systems with large datasets, prompt injection is trivial, and there's no access control—anyone with graph access sees everything.
• Most real-world graph operations (80%) fall into standard patterns like "get items from a list" or "update an item"—naming these as reusable queries eliminates the need for most people to write SPARQL at all.
• Named queries stored as RDF with metadata enable agents to self-discover available operations, support safe parameterization through text substitution (not SPARQL variables), enforce permissions locally, and log all access without redeploying code.

The article challenges RDF’s focus on global IRIs, showing how conversational and narrative contexts rely on deferred classification, consensus and scoped definitions rather than fixed identifiers. It outlines approaches—contextual definitions, master data management, local IRIs and self-learning AI—to build flexible, locally scoped ontologies.

• Real conversation shows meaning is built incrementally through context, not looked up via fixed global identifiers like RDF's IRIs/URIs
• Understanding of a term (like "bill") solidifies through accumulating clues and social consensus, mirroring how ontologies actually form in practice
• Different organizations naturally build conflicting local schemas for the same concept (e.g. "Customer"), so forcing one universal identifier without first aligning schemas creates clashes
• True interoperability comes from scoped, negotiated consensus between authorities rather than dumping all terms into one master global ontology