Schema linking is a task of investigating relevant SQL schema (items and paths) for a specific query language. Constructing a reliable linker is crucial for Text2SQL auto-coding since it provides informative clues for directing SQL coders. Current studies have obtained considerable performance gains when independently evaluating linkers, which allow F1-score to be higher than 90% at best. However, our observation illustrates that the existing linkers still suffers from a high missing rate of Bridge Path, i.e., omitting one or a series of SQL keywords that link across SQL tables. To address the issue, we propose to enhance linkers by Bridge Path Recovering (abbr., BPR) via Tentative Answer Profiling (TAP). On the one hand, BPR is implemented by learning to rank up the better bridged schemata during finetuning. On the other hand, TAP tentatively conjectures possible answers for the query language, and generates their profiles (types and CoT steps) to guide BPR. This is inspired by the experience that answer conjecturing contributes to reversely deducing the multi-hop routing path over SQL tables. Technologically, we train LLaMA-3.1 for TAP, where teacher-student distillation is conducted via Deepseek-3. To ensure sufficient ranking data used for training BPR, we employ a multilevel retrieval technique to align profiles with diverse schemata. We experiment on the benchmark datasets BIRD and Spider. The experimental results show that our approach (BPR-TAP) consistently yields improvements for F1-score upon all benchmarks, and surpasses multiple competitive schema linkers. All source codes are available at https://anonymous.4open.science/r/BPR-TAP-Text2SQL.

Text-to-SQL oriented table acquisition suffers from heterogeneous semantic gap. To address the issue, we propose a Reverse Engineering (RE) based optimization approach. Instead of forward table search using questions as queries, RE reversely generates potentially-matched question conditioned on table schemas, and promotes semantic consistency verification between homogeneous questions. We experiment on two benchmarks, including SpiderUnion and BirdUnion. The test results show that our approach yields substantial improvements compared to the Retrieval-Reranker (2R) baseline, and achieves competitive performance in both table acquisition and Text-to-SQL tasks.