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Dataset: lc_quad 🏷
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from datasets import load_dataset dataset = load_dataset("lc_quad")


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Table of Contents

Dataset Description

Dataset Summary

LC-QuAD 2.0 is a Large Question Answering dataset with 30,000 pairs of question and its corresponding SPARQL query. The target knowledge base is Wikidata and DBpedia, specifically the 2018 version. Please see our paper for details about the dataset creation process and framework.

Supported Tasks

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Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances


  • Size of downloaded dataset files: 3.69 MB
  • Size of the generated dataset: 19.77 MB
  • Total amount of disk used: 23.46 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

    "NNQT_question": "What is the {periodical literature} for {mouthpiece} of {Delta Air Lines}",
    "paraphrased_question": "What is Delta Air Line's periodical literature mouthpiece?",
    "question": "What periodical literature does Delta Air Lines use as a moutpiece?",
    "sparql_dbpedia18": "\"select distinct ?obj where { ?statement <> <",
    "sparql_wikidata": " select distinct ?obj where { wd:Q188920 wdt:P2813 ?obj . ?obj wdt:P31 wd:Q1002697 } ",
    "subgraph": "simple question right",
    "template": " <S P ?O ; ?O instanceOf Type>",
    "template_index": 65,
    "uid": 19719

Data Fields

The data fields are the same among all splits.


  • NNQT_question: a string feature.
  • uid: a int32 feature.
  • subgraph: a string feature.
  • template_index: a int32 feature.
  • question: a string feature.
  • sparql_wikidata: a string feature.
  • sparql_dbpedia18: a string feature.
  • template: a string feature.
  • paraphrased_question: a string feature.

Data Splits Sample Size

name train test
default 19293 4781

Dataset Creation

Curation Rationale

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Source Data

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

title={LC-QuAD 2.0: A Large Dataset for Complex Question Answering over Wikidata and DBpedia},
author={Dubey, Mohnish and Banerjee, Debayan and Abdelkawi, Abdelrahman and Lehmann, Jens},
booktitle={Proceedings of the 18th International Semantic Web Conference (ISWC)},