## 3 Materials and Methods
### Question Answering Method
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Datasets** & **TabFact** & **WikiTQ** & \multicolumn{2}{c}{**FeTaQA**} \\ \hline
**Metric** & **acc** & **acc** & **BLEU** & **ROUGE-1** \\ \hline TQAgent without knowledge inject & 69.71 & 48.81 & 27.47 & 0.61 \\ TQAgent with knowledge inject & 73.60 & 52.94 & 28.62 & 0.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Impact of Knowledge Graph injection on model performance. To retrieve the missing information, the system generates Neo4 query statements that search the Knowledge Graph for the corresponding country of each city. For instance, given a city name such as \"Paris\", the query retrieves the associated country, \"France\". Once the missing country data are obtained, they are injected back into the table, enriching the information and enabling more accurate reasoning. This case highlights the effectiveness of KG injection in enhancing table-based reasoning by dynamically expanding the available information. The process combines atomic table operations (e.g., row selection, column selection, and group statistics) with Knowledge Graph augmentation, enabling the system to verify statements more accurately and thoroughly. Figure 4: Example of KG injection. Figure 3: Examples of table dynamic actions. ## 5 Conclusions

This paper introduces TQAgent, a framework that combines Knowledge Graphs (KGs) and tree-structured reasoning to address hallucinations and limited reasoning in table-based question answering. TQAgent enhances model reliability by grounding reasoning in external world knowledge, dynamically sampling high-confidence paths, and enabling lightweight deployment through knowledge distillation with GPT-3.5. Experimental results show a 4% accuracy improvement on TabFact and WikiTQ, demonstrating its robustness across datasets and models. Future work will explore real-time KG updates and multimodal extensions, with resources made publicly available for reproducibility. This paper introduces TQAgent, a novel framework that integrates Knowledge Graphs (KGs) and tree-structured reasoning to address two critical challenges in table-based question answering (TableQA): hallucinations and limited reasoning capabilities. By grounding the reasoning process in external world knowledge, dynamically sampling high-confidence reasoning paths, and employing knowledge distillation techniques for lightweight deployment, TQAgent achieves significant improvements in both accuracy and computational efficiency. Experimental evaluations on three benchmark datasets--TabFact, WikiTQ, and FeTaQA--demonstrate the robustness of our approach. Specifically, TQAgent achieves up to a 4% accuracy improvement over baseline models on TabFact and WikiTQ, while also enhancing semantic alignment in natural language generation tasks on FeTaQA, as evidenced by a BLEU score increase of 4.2%. These results underscore the effectiveness of combining structured knowledge from KGs with dynamic operation planning and multi-step reasoning. The integration of Knowledge Graphs plays a pivotal role in mitigating hallucinations, particularly in scenarios involving implicit relationships or ambiguous information within tables. Additionally, the tree-structured reasoning mechanism enables the model to adapt to diverse table structures and complex reasoning tasks, offering a versatile solution for real-world applications. Looking ahead, several promising directions remain to be explored. First, enabling real-time updates of Knowledge Graphs could further enhance the model's ability to handle dynamic data environments. Second, extending TQAgent to support multimodal inputs (e.g., integrating visual and textual data) would broaden its applicability in domains like scientific research and business analytics. Finally, optimizing the framework for resource-constrained settings through advanced compression techniques will ensure scalability across different deployment scenarios. Conceptualization, J.Z., P.Z., Y.W., R.X., X.L., R.L. and S.L. ; project administration, P.Z., Z.O. and M.S. ; validation, X.L.