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

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Authors

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.

OriginalspracheEnglisch
TitelProceedings - 16th IEEE International Conference on Semantic Computing, ICSC 2022
Anzahl der Seiten6
VerlagInstitute of Electrical and Electronics Engineers Inc.
Erscheinungsdatum2022
Seiten229-234
ISBN (Print)978-1-6654-3419-5
ISBN (elektronisch)978-1-6654-3418-8
DOIs
PublikationsstatusErschienen - 2022
Extern publiziertJa
Veranstaltung16th IEEE International Conference on Semantic Computing, ICSC 2022 - Virtual, Online, USA / Vereinigte Staaten
Dauer: 26.01.202228.01.2022
http://pa.icar.cnr.it/scsn22/

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© 2022 IEEE.

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