QALD-9-plus: A Multilingual Dataset for Question Answering over DBpedia and Wikidata Translated by Native Speakers

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

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The ability to have the same experience for different user groups (i.e., accessibility) is one of the most important characteristics of Web-based systems. The same is true for Knowledge Graph Question Answering (KGQA) systems that provide the access to Semantic Web data via natural language interface. While following our research agenda on the multilingual aspect of accessibility of KGQA systems, we identified several ongoing challenges. One of them is the lack of multilingual KGQA benchmarks. In this work, we extend one of the most popular KGQA benchmarks - QALD-9 by introducing high-quality questions' translations to 8 languages provided by native speakers, and transferring the SPARQL queries of QALD-9 from DBpedia to Wikidata, s.t., the usability and relevance of the dataset is strongly increased. Five of the languages - Armenian, Ukrainian, Lithuanian, Bashkir and Belarusian - to our best knowledge were never considered in KGQA research community before. The latter two of the languages are considered as 'endangered' by UNESCO. We call the extended dataset QALD-9-plus and made it available online11Figshare: https://doi.org/10.6084/m9.figshare.16864273. GitHub: https://github.com/Perevalov/qald-9-plus.

Original languageEnglish
Title of host publicationProceedings - 16th IEEE International Conference on Semantic Computing, ICSC 2022
Number of pages6
PublisherInstitute of Electrical and Electronics Engineers Inc.
Publication date2022
Pages229-234
ISBN (print)978-1-6654-3419-5
ISBN (electronic)978-1-6654-3418-8
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event16th IEEE International Conference on Semantic Computing, ICSC 2022 - Virtual, Online, United States
Duration: 26.01.202228.01.2022
http://pa.icar.cnr.it/scsn22/

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