Standard
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, 2019. S. 69-78 (Lecture Notes in Computer Science; Band 11779 LNCS).
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
Harvard
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, 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_5APA
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.
https://doi.org/10.1007/978-3-030-30796-7_5Vancouver
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. 2019. S. 69-78. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-30796-7_5
Bibtex
@inbook{7c53083600b84c919c983f0ffefbd735,
title = "LC-QuAD 2.0: A Large Dataset for Complex Question Answering over Wikidata and DBpedia",
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.",
keywords = "Informatics",
author = "Mohnish Dubey and Debayan Banerjee and Abdelrahman Abdelkawi and Jens Lehmann",
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.; 18th International Semantic Web Conference - ISWC 2019, ISWC 2019 ; Conference date: 26-10-2019 Through 30-10-2019",
year = "2019",
doi = "10.1007/978-3-030-30796-7_5",
language = "English",
isbn = "978-3-030-30795-0",
volume = "2",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "69--78",
editor = "Chiara Ghidini and Olaf Hartig and Maria Maleshkova and Vojtech Sv{\'a}tek and Isabel Cruz and Aidan Hogan and Jie Song and Maxime Lefran{\c c}ois and Fabien Gandon",
booktitle = "The Semantic Web – ISWC 2019",
address = "Germany",
url = "https://iswc2019.semanticweb.org/, https://files.ifi.uzh.ch/ddis/iswc_archive/iswc/ab/2019/iswc2019.semanticweb.org/index.html",
}
RIS
TY - CHAP
T1 - LC-QuAD 2.0
T2 - 18th International Semantic Web Conference - ISWC 2019
AU - Dubey, Mohnish
AU - Banerjee, Debayan
AU - Abdelkawi, Abdelrahman
AU - Lehmann, Jens
N1 - Conference code: 18
PY - 2019
Y1 - 2019
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.
KW - Informatics
UR - http://www.scopus.com/inward/record.url?scp=85077909314&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-30796-7_5
DO - 10.1007/978-3-030-30796-7_5
M3 - Article in conference proceedings
AN - SCOPUS:85077909314
SN - 978-3-030-30795-0
VL - 2
T3 - Lecture Notes in Computer Science
SP - 69
EP - 78
BT - The Semantic Web – ISWC 2019
A2 - Ghidini, Chiara
A2 - Hartig, Olaf
A2 - Maleshkova, Maria
A2 - Svátek, Vojtech
A2 - Cruz, Isabel
A2 - Hogan, Aidan
A2 - Song, Jie
A2 - Lefrançois, Maxime
A2 - Gandon, Fabien
PB - Springer
CY - Cham
Y2 - 26 October 2019 through 30 October 2019
ER -
