. (2026). “A Neurosymbolic Approach to Enterprise Text-to-SQL Using a Schema-Level Meta Knowledge Graph”. 11th International Conference on Web Intelligence, Machine Intelligence and Semantics (WIMS 2026), Thessaloniki, Greece
Natural language interfaces promise to simplify access to enterprise data, but their use in regulated environments is complicated by large heterogeneous schemas, domain-specific semantics, and the limited reasoning capacity of lightweight on-premises language models. This paper presents a neurosymbolic approach to enterprise Text-to-SQL that combines such models with symbolic reasoning over a schema-level meta knowledge graph, separating natural language interpretation from deterministic query construction. A prototype is evaluated on semi-automatic knowledge graph construction, symbolic join reasoning, and end-to-end Text-to-SQL generation. The generated knowledge graph achieves high retrieval coverage, and symbolic join reasoning substantially improves execution accuracy while reducing performance variability compared with a purely neural baseline. Combining lightweight language models with symbolic reasoning therefore offers a robust route to natural language access to heterogeneous enterprise data under on-premises deployment constraints.