Due to the concise and structured nature of tables, the knowledge contained therein may be incomplete or missing, posing a significant challenge for table question answering (TableQA) and data analysis systems. Most existing datasets either fail to address the issue of external knowledge in TableQA or only utilize unstructured text as supplementary information for tables. In this paper, we propose to use a knowledge base (KB) as the external knowledge source for TableQA and construct a dataset KET-QA with fine-grained gold evidence annotation. Each table in the dataset corresponds to a sub-graph of the entire KB, and every question requires the integration of information from both the table and the sub-graph to be answered. To extract pertinent information from the vast knowledge sub-graph and apply it to TableQA, we design a retriever-reasoner structured pipeline model. Experimental results demonstrate that our model consistently achieves remarkable relative performance improvements ranging from 1.9 to 6.5 times and absolute improvements of 11.66% to 44.64% on EM scores across three distinct settings (fine-tuning, zero-shot, and few-shot), in comparison with solely relying on table information in the traditional TableQA manner. However, even the best model achieves a 60.23% EM score, which still lags behind the human-level performance, highlighting the challenging nature of KET-QA for the question-answering community. We also provide a human evaluation of error cases to analyze further the aspects in which the model can be improved. Project page: https://ketqa.github.io/.

翻译：由于表格具有简洁且结构化的特性，其中包含的知识可能存在不完整或缺失的问题，这给表格问答（TableQA）及数据分析系统带来了显著挑战。现有大多数数据集要么未能解决TableQA中的外部知识问题，要么仅利用非结构化文本作为表格的补充信息。本文提出采用知识库（KB）作为TableQA的外部知识来源，并构建了带有细粒度黄金证据标注的数据集KET-QA。数据集中每个表格对应整个知识库的一个子图，每个问题需整合表格与子图信息才能解答。为从庞大的知识子图中提取相关信息并应用于TableQA，我们设计了一个检索器-推理器结构的流水线模型。实验结果表明，与传统仅依赖表格信息的TableQA方法相比，我们的模型在三种不同设置（微调、零样本和少样本）下，EM分数始终取得1.9至6.5倍的相对性能提升以及11.66%至44.64%的绝对提升。然而，最佳模型仅达到60.23%的EM分数，仍落后于人类水平表现，凸显了KET-QA对问答领域的挑战性。我们还提供了错误案例的人工评估，以进一步分析模型可改进的方面。项目页面：https://ketqa.github.io/。