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RuBQ: A Russian Dataset for Question Answering over Wikidata [article]

Vladislav Korablinov, Pavel Braslavski
2020 arXiv   pre-print
The paper presents RuBQ, the first Russian knowledge base question answering (KBQA) dataset.  ...  The high-quality dataset consists of 1,500 Russian questions of varying complexity, their English machine translations, SPARQL queries to Wikidata, reference answers, as well as a Wikidata sample of triples  ...  We are grateful to Yandex.Toloka for their data annotation grant.  ... 
arXiv:2005.10659v1 fatcat:4fyptlackrafngtbehb4mlmkhm

RuBQ: A Russian Dataset for Question Answering over Wikidata [chapter]

Vladislav Korablinov, Pavel Braslavski
2020 Lecture Notes in Computer Science  
The paper presents RuBQ, the first Russian knowledge base question answering (KBQA) dataset.  ...  The high-quality dataset consists of 1,500 Russian questions of varying complexity, their English machine translations, SPARQL queries to Wikidata, reference answers, as well as a Wikidata sample of triples  ...  We are grateful to Yandex.Toloka for their data annotation grant.  ... 
doi:10.1007/978-3-030-62466-8_7 fatcat:bo2c5mp7unhhhbdxkuzfv5ujpy

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

Aleksandr Perevalov, Dennis Diefenbach, Ricardo Usbeck, Andreas Both
2022 arXiv   pre-print
The same is true for Knowledge Graph Question Answering (KGQA) systems that provide the access to Semantic Web data via natural language interface.  ...  of QALD-9 from DBpedia to Wikidata, s.t., the usability and relevance of the dataset is strongly increased.  ...  Additionally, authors would like to give thanks to Open Data Science community 11 for connecting data science enthusiasts all over the world.  ... 
arXiv:2202.00120v2 fatcat:lh47hpeo5be3tpx22tyd6gu7jy

MEKER: Memory Efficient Knowledge Embedding Representation for Link Prediction and Question Answering [article]

Viktoriia Chekalina, Anton Razzhigaev, Albert Sayapin, Evgeny Frolov, Alexander Panchenko
2022 arXiv   pre-print
We propose a MEKER, a memory-efficient KG embedding model, which yields SOTA-comparable performance on link prediction tasks and KG-based Question Answering.  ...  Furthermore, the size of KGs that may be useful in actual NLP assignments is enormous, and creating embedding over it has memory cost issues.  ...  Evgeny Frolov for providing advice and assistance in developing the MEKER method.  ... 
arXiv:2204.10629v2 fatcat:vbijfw6nkze3ne2iawfez2scd4

A Russian Jeopardy! Data Set for Question-Answering Systems [article]

Elena Mikhalkova
2021 arXiv   pre-print
Question answering (QA) is one of the most common NLP tasks that relates to named entity recognition, fact extraction, semantic search and some other fields.  ...  -like Russian QA data set collected from the official Russian quiz database Chgk (che ge ka). The data set includes 379,284 quiz-like questions with 29,375 from the Russian analogue of Jeopardy!  ...  .: Rubq: a russian dataset for question answering over wikidata. In: International Semantic Web Conference. pp. 97–110. Springer (2020) 8.  ... 
arXiv:2112.02325v1 fatcat:v2hur2vu7bcc7kesj2yjcl6p2q

Can Machine Translation be a Reasonable Alternative for Multilingual Question Answering Systems over Knowledge Graphs?

Aleksandr Perevalov, Andreas Both, Dennis Diefenbach, Axel-Cyrille Ngonga Ngomo
2022 Proceedings of the ACM Web Conference 2022  
For the intensive evaluation, we extend the QALD-9 dataset for KGQA with Wikidata queries and high-quality translations.  ...  By using multiple KGQA systems for the evaluation, we were enabled to investigate and answer the main research question: "Can MT be an alternative for multilingual KGQA systems?".  ...  Additionally, authors would like to give thanks to Open Data Science community 12 for connecting data science enthusiasts all over the world.  ... 
doi:10.1145/3485447.3511940 fatcat:o4uae3zfnzhd7hmochxwanih3a

Compositional Generalization in Multilingual Semantic Parsing over Wikidata [article]

Ruixiang Cui, Rahul Aralikatte, Heather Lent, Daniel Hershcovich
2022 arXiv   pre-print
We propose a method for creating a multilingual, parallel dataset of question-query pairs, grounded in Wikidata.  ...  We introduce such a dataset, which we call Multilingual Compositional Wikidata Questions (MCWQ), and use it to analyze the compositional generalization of semantic parsers in Hebrew, Kannada, Chinese and  ...  Acknowledgments The authors thank Anders Søgaard and Miryam de Lhoneux for their comments and suggestions, as well as the TACL editors and several rounds of reviewers for their constructive evaluation.  ... 
arXiv:2108.03509v2 fatcat:l5i3vozpxre6pn7lnz2dwfddwq

Improving the Question Answering Quality using Answer Candidate Filtering based on Natural-Language Features [article]

Aleksandr Gashkov, Aleksandr Perevalov, Maria Eltsova, Andreas Both
2021 arXiv   pre-print
However, the quality of the included Question Answering (QA) functionality is still not sufficient regarding the number of questions that are answered correctly.  ...  Hence, filtering incorrect answers from a list of answer candidates is leading to a highly improved QA quality.  ...  QAnswer provides an API to ask a question and receive the corresponding ranked query candidate list. It supports questions over several knowledge bases including Wikidata.  ... 
arXiv:2112.05452v1 fatcat:2c6m76oiwrgjrpjbolinswloli

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

Aleksandr Perevalov, Dennis Diefenbach, Ricardo Usbeck, Andreas Both
2022
The same is true for Knowledge Graph Question Answering (KGQA) systems that provide the access to Semantic Web data via natural language interface.  ...  of QALD-9 from DBpedia to Wikidata, s.t., the usability and relevance of the dataset is strongly increased.  ...  Additionally, authors would like to give thanks to Open Data Science community 11 for connecting data science enthusiasts all over the world.  ... 
doi:10.48550/arxiv.2202.00120 fatcat:4ukfoao7dnaftgwc5mxgyxlhzu

MEKER: Memory Efficient Knowledge Embedding Representation for Link Prediction and Question Answering

Viktoriia Chekalina, Anton Razzhigaev, Albert Sayapin, Evgeny Frolov, Alexander Panchenko
2022 Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop   unpublished
We propose a MEKER, a memory-efficient KG embedding model, which yields SOTA-comparable performance on link prediction tasks and KGbased Question Answering.  ...  Furthermore, the size of KGs that may be useful in actual NLP assignments is enormous, and creating embedding over it has memory cost issues.  ...  RuBQ 2.0 is a Russian language QA benchmark with multiple types of questions aligned with Wikidata. For both SimpleQuestions and RuBQ, for each question, an answer is represented by a KG triple.  ... 
doi:10.18653/v1/2022.acl-srw.27 fatcat:h47jpjcggva2zntbw234qk4uz4