HiR-MKR: Hierarchical Reconstruction and Multi-Source Knowledge Retrieval for Semi-Structured Table Question Answering

Semi-structured tables are widely used in real-world applications, but complex layouts such as multi-level headers, merged cells, and nested subtables pose significant challenges to table question answering. ST-Raptor provides structured representations through Hierarchical Orthogonal Trees (HO-Trees), but their limited explicit hierarchical semantics and lack of complementary knowledge representations constrain effective retrieval and reasoning. We propose HiR-MKR, a semi-structured table question answering framework that integrates hierarchical structure reconstruction with multi-source knowledge retrieval. Table Adaptive Hierarchical Reconstruction Algorithm (TAHRA) reconstructs explicit hierarchical structures from HO-Trees and provides a unified basis for a knowledge graph, a Record vector store, and a Path vector store. HiR-MKR further performs query decomposition and type-aware, dependency-driven retrieval and reasoning, with structure reliability assessment to control reliance on reconstructed knowledge. Experiments on WikiTQ-ST, TempTabQA-ST, and SSTQA show that HiR-MKR consistently outperforms existing methods, achieving 82.72% accuracy on SSTQA and outperforming ST-Raptor by 10.33 percentage points. It also reduces average response time and total token consumption by 59.29% and 73.00%, respectively.