QALD-9-plus is the dataset for Knowledge Graph Question Answering (KGQA) based on well-known QALD-9.

QALD-9-plus enables to train and test KGQA systems over DBpedia and Wikidata using questions in 9 different languages: English, German, Russian, French, Armenian, Belarusian, Lithuanian, Bashkir, and Ukrainian.

Some of the questions have several alternative writings in particular languages which enables to evaluate the robustness of KGQA systems and train paraphrasing models.

As the questions' translations were provided by native speakers, they are considered as "gold standard", therefore, machine translation tools can be trained and evaluated on the dataset.

Given the numbers, it is obvious that some of the languages are covered more than once i.e., there is more than one translation for a particular question. For example, there are 1203 Russian translations available while only 408 unique questions exist in the training subset (i.e., 2.9 Russian translations per one question). The availability of such parallel corpora enables the researchers, developers and other dataset users to address the paraphrasing task.

We used GERBIL system for the evaluation of the dataset. The detailed information for the experiments is available at the individual link (click the value in the cells).

@inproceedings{perevalov2022qald9plus,
      author={Perevalov, Aleksandr and Diefenbach, Dennis and Usbeck, Ricardo and Both, Andreas},
      booktitle={2022 IEEE 16th International Conference on Semantic Computing (ICSC)},
      title={QALD-9-plus: A Multilingual Dataset for Question Answering over DBpedia and Wikidata Translated by Native Speakers},
      year={2022},
      pages={229-234},
      doi={10.1109/ICSC52841.2022.00045}
}

• ArXiv link
• Papers with Code: Paper, Dataset
• Video presentation on YouTube: https://youtu.be/W1w7CJTV48c
• Presentation slides
• Google Colab notebook

This work is licensed under a Creative Commons Attribution 4.0 International License.

The following table is necessary for this dataset to be indexed by search engines such as Google Dataset Search.