TACKLE: QUESTION ANSWERING OVER PRACTICAL ENTERPRISE TABLES VIA CONCEPTUAL KNOWLEDGE GRAPHS

Tabular data is a primary source of enterprise knowledge, yet it is typically scattered across independently maintained tables. While large language models enable natural-language question answering over tables, existing approaches—such as formal query-based and retrieval-based methods—rely heavily on explicit relational schemas, well-aligned and clear column names, and normalized layouts. These assumptions, however, often do not hold for practical enterprise tables, making enterprise reasoning extremely challenging. To bridge this gap, we propose TACKLE, a framework that operates independently of any predefined schema or ontology. TACKLE directly induces a conceptual knowledge graph from practical tables by clustering related columns into enterprise concepts and establishing explicit semantic relations. Building upon this graph-based representation, TACKLE employs a decoupled NL-to-Cypher translation pipeline that separates high-level semantic planning from low-level syntactic rendering to improve the query reliability. We further introduce ERP-QA, a controllable and extensible benchmark generation framework that reproduces these practical table characteristics and provides deterministically verified answers. Experiments on an evaluation set instantiated from ERP-QA and a practical variant of MMQA show that TACKLE consistently outperforms representative formal query-based and retrieval-based baselines across multiple LLM backbones. Ablation studies further support the benefits of the conceptual KG and decoupled translation for practical multi-table QA. Our source code is available at https://anonymous.4open.science/r/TACKLE-4798/README.md.