LC-QuAD: A Corpus for Complex Question Answering over Knowledge Graphs

By Priyansh Trivedi, Gaurav Maheshwari, Mohnish Dubey, Jens Lehmann. International Workshop on the Semantic Web, 2017

295 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/ef2281819cc92401e8488ca735794b2f46e319d0

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In Artificial Intelligence

1. Philip S. Yu
2. Erik Cambria
3. Shirui Pan
4. Percy Liang
5. Yu Su
6. Soujanya Poria
7. Gerhard Weikum
8. Xifeng Yan

Citations in the corpus

• TempQuestions: A Benchmark for Temporal Question Answering (2018, methodology): “The LC-QuAD dataset [28] contains 5 , 000 questions and their corresponding SPARQL queries over DBpedia.”
• Explanations for CommonsenseQA: New Dataset and Models (2021, background): “…QA is focused on either (i) proposing a new kind of dataset (Unger et al., 2014; Rajpurkar et al., 2016; Ling et al., 2017; Joshi et al., 2017; Trivedi et al., 2017; Welbl et al., 2017; Yang et al., 2018; Kwiatkowski et al., 2019; Talmor et al., 2019; Miao et al., 2020), or (ii) proposing a…”
• Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases (2020, background): “assumption because their test set is a randomly sampled subset of all the data [4, 6, 39, 40, 45].”
• A Survey on Knowledge Graphs: Representation, Acquisition, and Applications (2020, background): “Numerous downstream knowledge-aware applications also come up with many datasets, for example, WikiFacts [203] for language modeling; SimpleQuestions [155] and LC-QuAD [204] for question answering; and Freebase Semantic Scholar [179] for academic search.”
• ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters (2018, background): “…utilize Free917 (Cai and Yates, 2013) WebQuestions (Berant et al., 2013) SimpleQuestions (Bordes et al., 2015) QALD (Usbeck et al., 2017) LC-QuAD (Trivedi et al., 2017) ComplexQuestions (Bao et al., 2016) GraphQuestions (Su et al., 2016) ComplexWebQuestions (Talmor and Berant, 2018) TREC…”
• PYTHIA: Universal Question Answering over Knowledge Graphs (2026, unknown): “We also test QALD-9 [4] and LC-QuAD [39] for KGQA performance over DBpedia [1].”