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

Mohnish Dubey; Debayan Banerjee; Abdelrahman Abdelkawi; Jens Lehmann

doi:10.1007/978-3-030-30796-7_5

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

Mohnish Dubey^*
, Debayan Banerjee
, Abdelrahman Abdelkawi
, Jens Lehmann

^*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › Begutachtung

204 Zitate (Scopus)

Abstract

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.

Originalsprache	Englisch
Titel	The Semantic Web – ISWC 2019 : 18th International Semantic Web Conference, Auckland, New Zealand, October 26-30, 2019 : proceedings
Redakteure/-innen	Chiara Ghidini, Olaf Hartig, Maria Maleshkova, Vojtech Svátek, Isabel Cruz, Aidan Hogan, Jie Song, Maxime Lefrançois, Fabien Gandon
Seitenumfang	10
Band	2
Erscheinungsort	Cham
Herausgeber (Verlag)	Springer Verlag
Erscheinungsdatum	2019
Seiten	69-78
ISBN (Print)	978-3-030-30795-0
ISBN (elektronisch)	978-3-030-30796-7
DOIs	https://doi.org/10.1007/978-3-030-30796-7_5
Publikationsstatus	Erschienen - 2019
Extern publiziert	Ja
Veranstaltung	18th International Semantic Web Conference - ISWC 2019: The premier international forum for the Semantic Web and Linked Data Community - University of Auckland, Auckland, Neuseeland Dauer: 26.10.2019 → 30.10.2019 Konferenznummer: 18 https://iswc2019.semanticweb.org/ https://files.ifi.uzh.ch/ddis/iswc_archive/iswc/ab/2019/iswc2019.semanticweb.org/index.html

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© 2019, Springer Nature Switzerland AG.

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Dubey, M., Banerjee, D., Abdelkawi, A., & Lehmann, J. (2019). LC-QuAD 2.0: A Large Dataset for Complex Question Answering over Wikidata and DBpedia. in C. Ghidini, O. Hartig, M. Maleshkova, V. Svátek, I. Cruz, A. Hogan, J. Song, M. Lefrançois, & F. Gandon (Hrsg.), The Semantic Web – ISWC 2019 : 18th International Semantic Web Conference, Auckland, New Zealand, October 26-30, 2019 : proceedings (Band 2, S. 69-78). (Lecture Notes in Computer Science; Band 11779 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-30796-7_5

Dubey, Mohnish ; Banerjee, Debayan ; Abdelkawi, Abdelrahman et al. / LC-QuAD 2.0 : A Large Dataset for Complex Question Answering over Wikidata and DBpedia. The Semantic Web – ISWC 2019 : 18th International Semantic Web Conference, Auckland, New Zealand, October 26-30, 2019 : proceedings. Hrsg. / Chiara Ghidini ; Olaf Hartig ; Maria Maleshkova ; Vojtech Svátek ; Isabel Cruz ; Aidan Hogan ; Jie Song ; Maxime Lefrançois ; Fabien Gandon. Band 2 Cham : Springer Verlag, 2019. S. 69-78 (Lecture Notes in Computer Science).

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abstract = "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.",

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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: {\textcopyright} 2019, Springer Nature Switzerland AG.; The 18th International Semantic Web Conference - ISWC 2019 : The premier international forum for the Semantic Web and Linked Data Community, ISWC 2019 ; Conference date: 26-10-2019 Through 30-10-2019",

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Dubey, M, Banerjee, D, Abdelkawi, A & Lehmann, J 2019, LC-QuAD 2.0: A Large Dataset for Complex Question Answering over Wikidata and DBpedia. in C Ghidini, O Hartig, M Maleshkova, V Svátek, I Cruz, A Hogan, J Song, M Lefrançois & F Gandon (Hrsg.), The Semantic Web – ISWC 2019 : 18th International Semantic Web Conference, Auckland, New Zealand, October 26-30, 2019 : proceedings. Bd. 2, Lecture Notes in Computer Science, Bd. 11779 LNCS, Springer Verlag, Cham, S. 69-78, 18th International Semantic Web Conference - ISWC 2019, Auckland, Neuseeland, 26.10.19. https://doi.org/10.1007/978-3-030-30796-7_5

LC-QuAD 2.0: A Large Dataset for Complex Question Answering over Wikidata and DBpedia. / Dubey, Mohnish; Banerjee, Debayan; Abdelkawi, Abdelrahman et al.
The Semantic Web – ISWC 2019 : 18th International Semantic Web Conference, Auckland, New Zealand, October 26-30, 2019 : proceedings. Hrsg. / Chiara Ghidini; Olaf Hartig; Maria Maleshkova; Vojtech Svátek; Isabel Cruz; Aidan Hogan; Jie Song; Maxime Lefrançois; Fabien Gandon. Band 2 Cham: Springer Verlag, 2019. S. 69-78 (Lecture Notes in Computer Science; Band 11779 LNCS).

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › Begutachtung

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AU - Lehmann, Jens

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N2 - 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.

AB - 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.

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A2 - Cruz, Isabel

A2 - Hogan, Aidan

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Dubey M, Banerjee D, Abdelkawi A, Lehmann J. LC-QuAD 2.0: A Large Dataset for Complex Question Answering over Wikidata and DBpedia. in Ghidini C, Hartig O, Maleshkova M, Svátek V, Cruz I, Hogan A, Song J, Lefrançois M, Gandon F, Hrsg., The Semantic Web – ISWC 2019 : 18th International Semantic Web Conference, Auckland, New Zealand, October 26-30, 2019 : proceedings. Band 2. Cham: Springer Verlag. 2019. S. 69-78. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-30796-7_5

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

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