  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/.
