Why Semantic Layers Outperform Graph Ontologies in LLM-Based Text-to-SQL Systems
Text-to-SQL has not failed because models cannot write SQL. It has failed because we keep giving Large Language Models the wrong kind of context.
For years, the industry has relied on ER diagrams, ontologies, and graph-based schema models to explain databases to machines. These tools were built for humans to visualise structure, not for language models that reason using tokens, descriptions, and probability.
As enterprise benchmarks such as BIRD and Spider have demonstrated, graph-heavy systems struggle the moment schemas become messy, ambiguous, or truly enterprise-grade.
The most accurate modern Text-to-SQL systems have quietly shifted direction:
- They prune schemas instead of visualizing everything
- They inject semantic descriptions instead of traversing graphs
- They reason over language rather than rigid relationships
This is where semantic layers begin to replace ontologies, not as documentation, but as a core execution layer designed for LLM reasoning.
In our new whitepaper, Semantic Layer: Terno Approach vs Graph Ontology, we explain:
- Why ontology-based systems structurally fail LLM reasoning
- How automated semantic layers outperform graphs on enterprise data
- Why top-performing Text-to-SQL architectures align with semantic narration rather than ontology traversal
- What this shift means for enterprises investing in AI-driven analytics
Read the full whitepaper here: Semantic_Layer_Terno_Approach_vs_Graph_Ontology
For the enterprise seeking to unlock the value of its data, the directive is therefore unambiguous. Do not invest in tools that merely visualise your schema that freeze meaning in time. Invest instead in systems that can automatically understand, narrate, and evolve your data.
