Knowledge Graph Question Answering Leaderboard: A Community Resource to Prevent a Replication Crisis
Abstract: 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.
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@inproceedings{DBLP:conf/lrec/PerevalovYKJ0U22,
author = {Aleksandr Perevalov and
Xi Yan and
Liubov Kovriguina and
Longquan Jiang and
Andreas Both and
Ricardo Usbeck},
editor = {Nicoletta Calzolari and
Fr{\'{e}}d{\'{e}}ric B{\'{e}}chet and
Philippe Blache and
Khalid Choukri and
Christopher Cieri and
Thierry Declerck and
Sara Goggi and
Hitoshi Isahara and
Bente Maegaard and
Joseph Mariani and
H{\'{e}}l{\`{e}}ne Mazo and
Jan Odijk and
Stelios Piperidis},
title = {Knowledge Graph Question Answering Leaderboard: {A} Community Resource
to Prevent a Replication Crisis},
booktitle = {Proceedings of the Thirteenth Language Resources and Evaluation Conference,
{LREC} 2022, Marseille, France, 20-25 June 2022},
pages = {2998--3007},
publisher = {European Language Resources Association},
year = {2022},
url = {https://aclanthology.org/2022.lrec-1.321},
timestamp = {Wed, 04 Jan 2023 16:55:44 +0100},
biburl = {https://dblp.org/rec/conf/lrec/PerevalovYKJ0U22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}