LC-QuAD 2.0: A Large Dataset for Complex Question Answering over Wikidata and DBpedia
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-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 language | English |
|---|---|
| Title of host publication | The Semantic Web – ISWC 2019 : 18th International Semantic Web Conference, Auckland, New Zealand, October 26-30, 2019 : proceedings |
| Editors | Chiara Ghidini, Olaf Hartig, Maria Maleshkova, Vojtech Svátek, Isabel Cruz, Aidan Hogan, Jie Song, Maxime Lefrançois, Fabien Gandon |
| Number of pages | 10 |
| Volume | 2 |
| Place of Publication | Cham |
| Publisher | Springer |
| Publication date | 2019 |
| Pages | 69-78 |
| ISBN (print) | 978-3-030-30795-0 |
| ISBN (electronic) | 978-3-030-30796-7 |
| DOIs | |
| Publication status | Published - 2019 |
| Externally published | Yes |
| Event | 18th International Semantic Web Conference - ISWC 2019 - Auckland, New Zealand Duration: 26.10.2019 → 30.10.2019 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.
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