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Data-driven systems need to be evaluated to establish trust in the scientificapproach and its applicability. In particular, this is true for Knowledge Graph(KG) Question Answering (QA), where complex data structures are made accessiblevia natural-language interfaces. Evaluating the capabilities of these systemshas been a driver for the community for more than ten years while establishingdifferent KGQA benchmark datasets. However, comparing different approaches iscumbersome. The lack of existing and curated leaderboards leads to a missingglobal 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-QuADand 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 significantcoverage of 100 publications and 98 systems from the last decade. We provide anew central and open leaderboard for any KGQA benchmark dataset as a focalpoint for the community - https://kgqa.github.io/leaderboard. Our analysishighlights existing problems during the evaluation of KGQA systems. Thus, wewill point to possible improvements for future evaluations.

Knowledge Graph Question Answering Leaderboard: A Community Resource to Prevent a Replication Crisis