  Question answering over knowledge bases (KBQA) has become a popular approach
to help users extract information from knowledge bases. Although several
systems exist, choosing one suitable for a particular application scenario is
difficult. In this article, we provide a comparative study of six
representative KBQA systems on eight benchmark datasets. In that, we study
various question types, properties, languages, and domains to provide insights
on where existing systems struggle. On top of that, we propose an advanced
mapping algorithm to aid existing models in achieving superior results.
Moreover, we also develop a multilingual corpus COVID-KGQA, which encourages
COVID-19 research and multilingualism for the diversity of future AI. Finally,
we discuss the key findings and their implications as well as performance
guidelines and some future improvements. Our source code is available at
\url{https://github.com/tamlhp/kbqa}.
