Ontology

8. From 66% to 80% NL2SQL Accuracy: Four Measure-and-Fix Loops

After wiring up the router, I ran a 30-question benchmark and pushed NL2SQL EX (Execution Accuracy) from 66.67% to 80%. Here’s what I fixed across four cycles and where things broke.

7. When a Question Comes In, Who Decides the Routing?

The term definitions are done. But when a user asks a question, who decides whether to search the graph, write SQL, or run a vector search? Things I ran into while designing the router.

6. When You Don't Have to Build Agent Infra Yourself, Harnesses Become Obsolete. What About the Ontology?

Shortly after the Conway leak, Anthropic officially launched Claude Managed Agents. As agent infrastructure gets absorbed into platforms, here’s why DataNexus’s ontology layer remains safe.

5. Automating Metadata Maintenance: Karpathy's LLM Wiki Architecture

RAG starts from scratch every time. Karpathy proposes having the LLM maintain a wiki directly so knowledge accumulates. DataNexus’s ontology catalog needs the same principle to avoid abandonment.

4. Why We Added a SKOS Compatibility Layer

Why we chose SKOS to connect the DataNexus ontology with external systems. Designing a compatibility layer between LPG and RDF – two different graph models.

3. Can DataHub's Glossary Work as an Ontology?

We tried using DataHub’s Business Glossary as an ontology store. What worked, what didn’t, and how we worked around it.