  Data-driven systems need to be evaluated to establish trust in the scientific
approach and its applicability. In particular, this is true for Knowledge Graph
(KG) Question Answering (QA), where complex data structures are made accessible
via natural-language interfaces. Evaluating the capabilities of these systems
has been a driver for the community for more than ten years while establishing
different KGQA benchmark datasets. However, comparing different approaches is
cumbersome. The lack of existing and curated leaderboards leads to a missing
global view over the research field and could inject mistrust into the results.
In particular, the latest and most-used datasets in the KGQA community, LC-QuAD
and QALD, miss providing central and up-to-date points of trust. In this paper,
we survey and analyze a wide range of evaluation results with significant
coverage of 100 publications and 98 systems from the last decade. We provide a
new central and open leaderboard for any KGQA benchmark dataset as a focal
point for the community - https://kgqa.github.io/leaderboard. Our analysis
highlights existing problems during the evaluation of KGQA systems. Thus, we
will point to possible improvements for future evaluations.
