Multi-Table Retrieval Method Based on Implicit Association Reasoning in the Petroleum Domain

In the digital transformation of the petroleum industry, massive multi-source heterogeneous tables are distributed across databases, Word documents, PDF reports, and engineering systems. Their unstructured format and weakly expressed inter-table relationships make it difficult for conventional keyword-based or single-table retrieval methods to locate the complete set of tables needed for complex queries. To address this problem, this paper proposes Relatab, a multi-table retrieval framework based on implicit association reasoning. Relatab first estimates query–table relevance through a dual-level semantic matching mechanism that combines table-level signals, including captions and column names, with value-level signals weighted by entropy and CRITIC criteria. It then constructs an implicit table graph using column-name and column-content similarity, and applies a max-product multi-hop propagation rule with decay and pruning to identify complementary tables that are not directly matched by the query. Finally, direct relevance and inter-table complementarity are fused to produce the retrieved table set. Experiments on Spider, Bird, and CementingTables show that Relatab achieves Top-2 recall rates of 79.21%, 61.26%, and 77.88%, respectively, outperforming DTR by 1.74, 2.33, and 1.85 percentage points. The results indicate that explicit modeling of implicit inter-table associations improves retrieval coverage in complex multi-table scenarios while remaining applicable to petroleum-domain documents.