QALD-9-plus: A Multilingual Dataset for Question Answering over DBpedia and Wikidata Translated by Native Speakers
This work addresses the multilingual accessibility problem for KGQA researchers by providing a new dataset, though it is incremental as it builds upon an existing benchmark.
The authors tackled the lack of multilingual benchmarks in Knowledge Graph Question Answering (KGQA) by extending the QALD-9 dataset with high-quality translations to 8 languages, including 5 previously unconsidered ones, and transferring SPARQL queries from DBpedia to Wikidata to enhance usability and relevance.
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 online https://github.com/Perevalov/qald_9_plus.