To bridge the semantic gap in NL2SQL (Natural Language to SQL) tasks, this study proposes a "Question→SQL→Wiki" framework that leverages a dynamic Wiki Graph as an intermediate reasoning layer. Departing from conventional NL2SQL approaches that rely solely on end-to-end mapping, our method utilizes Large Language Models (LLMs) to translate natural language questions into SQL queries, subsequently parsing these queries to construct a context-specific Wiki Graph. Unlike static knowledge graphs, this architecture emulates the associative and incremental nature of encyclopedic systems, explicitly modeling both database schemas and query-specific logic. By transforming abstract linguistic intent into a structured, wiki-style representation prior to execution, the framework significantly enhances schema linking and logical correctness. Experiments demonstrate that this graph-augmented paradigm improves the accuracy and robustness of NL2SQL systems, offering a scalable solution for complex query generation over relational databases.
Jia-Xuan Liu, Shi-Yu Fang, Jibing Wu et al.· 2026 12th International Conf...· 0 citations
Large language models (LLMs) have rapidly advanced natural-language-to-query (Text-to-Query) capabilities, yet existing public benchmarks remain confined to single database paradigms such as Text-to-SQL or Text-to-KG. They do not capture real-world settings where relational databases, graph databases, document databases, and NoSQL systems coexist. We propose a template-driven method for constructing a multi-database Text-to-Query benchmark targeting heterogeneous data lakes. Our framework introduces a unified semantic entity space anchored by a Global Entity Registry (GER) that maps local identifiers across SQL, Neo4j, MongoDB, and document retrieval systems. From this representation we design 400 query templates and generate approximately 18380 cross-database instances via template-driven entity sampling. Every instance passes execution-level verification and structural consistency validation. The resulting dataset supports single-database queries, cross-database reasoning, and multi-source fusion tasks, offering a standardized resource for evaluating LLMs under heterogeneous data conditions. The benchmark is built on a fully synthetic hierarchical-organization scenario; the template-driven generation method and GER-based entity alignment are domain-agnostic and directly applicable to any multi-model data environment.
Guo-Shen Li, Hang Zhang, Ying-Jun Liu et al.· 2026 12th International Conf...· 0 citations
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