LC-QuAD: A Corpus for Complex Question Answering over Knowledge Graphs
Open accessAbstract
Being able to access knowledge bases in an intuitive way has been an active area of research over the past years. In particular, several question answering (QA) approaches which allow to query RDF datasets in natural language have been developed as they allow end users to access knowledge without needing to learn the schema of a knowledge base and learn a formal query language. To foster this research area, several training datasets have been created, e.g. in the QALD (Question Answering over Linked Data) initiative. However, existing datasets are insufficient in terms of size, variety or complexity to apply and evaluate a range of machine learning based QA approaches for learning complex SPARQL queries. With the provision of the Large-Scale Complex Question Answering Dataset (LC-QuAD), we close this gap by providing a dataset with 5000 questions and their corresponding SPARQL queries over the DBpedia dataset. In this article, we describe the dataset creation process and how we ensure a high variety of questions, which should enable to assess the robustness and accuracy of the next generation of QA systems for knowledge graphs. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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LC-QuAD 1.0 contains 5,000 questions and corresponding SPARQL queries over DBpedia version 2016-04.
“LC-QuAD 1.0 (Trivedi et al., 2017) is a large complex questions dataset for KBQA, which contains 5,000 questions and corresponding SPARQL queries over DBpedia (version 2016-04).”
From Implicit Relation Linking for Question Answering over Knowledge Graph (2022) · cites this paper as Trivedi et al., 2017
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A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3070843 · Cited by 2,947 · see citing works
A Semantic Parsing and Reasoning-Based Approach to Knowledge Base Question Answering
Abdelaziz, I., Ravishankar, S., Kapanipathi, P., Roukos, S., & Gray, A. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i18.17988 · Cited by 26 · see citing works
“To evaluate DTQA , we use two prominent KBQA datasets; QALD - 9 (Ngomo 2018) and LC-QuAD 1.0 (Trivedi et al. 2017).”
MethodInfluentialImplicit Relation Linking for Question Answering over Knowledge Graph
Zhao, Y., Huang, J., Hu, W., Chen, Q., Qiu, X., Huo, C., & Ren, W. (2022) · Findings of the Association for Computational Linguistics: ACL 2022 · doi:10.18653/v1/2022.findings-acl.312 · Cited by 5 · see citing works
- Authors
- Trivedi, P., Maheshwari, G., Dubey, M., & Lehmann, J.
- Affiliations
- University of Bonn (DE)
- Corresponding author
- Trivedi, Priyansh
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 210-218
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2763039547
- Cited by
- 261
- References
- 1
- Citations by year
- 2026: 15 · 2025: 19 · 2024: 21 · 2023: 37 · 2022: 35 · 2021: 59 · 2020: 28 · 2019: 30 · 2018: 15 · 2017: 2
- FWCI
- 9.36
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Lecture notes in computer science (publishedVersion, cc-by)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- complex question answering, knowledge graphs, natural language question answering, RDF datasets, DBpedia, dataset construction
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