LC-QuAD 2.0: A Large Dataset for Complex Question Answering over Wikidata and DBpedia

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

Authors

Providing machines with the capability of exploring knowledge graphs and answering natural language questions has been an active area of research over the past decade. In this direction translating natural language questions to formal queries has been one of the key approaches. To advance the research area, several datasets like WebQuestions, QALD and LCQuAD have been published in the past. The biggest data set available for complex questions (LCQuAD) over knowledge graphs contains five thousand questions. We now provide LC-QuAD 2.0 (Large-Scale Complex Question Answering Dataset) with 30,000 questions, their paraphrases and their corresponding SPARQL queries. LC-QuAD 2.0 is compatible with both Wikidata and DBpedia 2018 knowledge graphs. In this article, we explain how the dataset was created and the variety of questions available with examples. We further provide a statistical analysis of the dataset. Resource Type: Dataset Website and documentation: http://lc-quad.sda.tech/ Permanent URL: https://figshare.com/projects/LCQuAD_2_0/62270.

Original languageEnglish
Title of host publicationThe Semantic Web – ISWC 2019 : 18th International Semantic Web Conference, Auckland, New Zealand, October 26-30, 2019 : proceedings
EditorsChiara Ghidini, Olaf Hartig, Maria Maleshkova, Vojtech Svátek, Isabel Cruz, Aidan Hogan, Jie Song, Maxime Lefrançois, Fabien Gandon
Number of pages10
Volume2
Place of PublicationCham
PublisherSpringer
Publication date2019
Pages69-78
ISBN (print)978-3-030-30795-0
ISBN (electronic)978-3-030-30796-7
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event18th International Semantic Web Conference - ISWC 2019 - Auckland, New Zealand
Duration: 26.10.201930.10.2019
Conference number: 18
https://iswc2019.semanticweb.org/
https://files.ifi.uzh.ch/ddis/iswc_archive/iswc/ab/2019/iswc2019.semanticweb.org/index.html

Bibliographical note

Funding Information:
Acknowledgements. This work has mainly been supported by the Fraunhofer-Cluster of Excellence “Cognitive Internet Technologies” (CCIT). It has also partly been supported by the German Federal Ministry of Education and Research (BMBF) in the context of the research project “InclusiveOCW” (grant no. 01PE17004D).

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.