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

Aleksandr Perevalov, Xi Yan, Liubov Kovriguina, Longquan Jiang, Andreas Both, Ricardo Usbeck


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.
Anthology ID:
2022.lrec-1.321
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
2998–3007
Language:
URL:
https://aclanthology.org/2022.lrec-1.321
DOI:
Bibkey:
Cite (ACL):
Aleksandr Perevalov, Xi Yan, Liubov Kovriguina, Longquan Jiang, Andreas Both, and Ricardo Usbeck. 2022. Knowledge Graph Question Answering Leaderboard: A Community Resource to Prevent a Replication Crisis. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 2998–3007, Marseille, France. European Language Resources Association.
Cite (Informal):
Knowledge Graph Question Answering Leaderboard: A Community Resource to Prevent a Replication Crisis (Perevalov et al., LREC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.321.pdf
Code
 kgqa/leaderboard
Data
WebQuestions