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lc_quad2-sparqltotext

Tasks:
Question Answering
Text Generation
Modalities:
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Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
Tags:
qa
knowledge-graph
sparql
Libraries:
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pandas
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metadata
dataset_info:
  features:
    - name: uid
      dtype: int32
    - name: NNQT_question
      dtype: string
    - name: paraphrased_question
      dtype: string
    - name: question
      dtype: string
    - name: simplified_query
      dtype: string
    - name: sparql_dbpedia18
      dtype: string
    - name: sparql_wikidata
      dtype: string
    - name: answer
      list: string
    - name: solved_answer
      list: string
    - name: subgraph
      dtype: string
    - name: template
      dtype: string
    - name: template_id
      dtype: string
    - name: template_index
      dtype: int32
  splits:
    - name: train
      num_bytes: 241621115
      num_examples: 21101
    - name: validation
      num_bytes: 11306539
      num_examples: 3010
    - name: test
      num_bytes: 21146458
      num_examples: 6024
  download_size: 79003648
  dataset_size: 274074112
task_categories:
  - question-answering
  - text-generation
tags:
  - qa
  - knowledge-graph
  - sparql
language:
  - en

Dataset Card for LC-QuAD 2.0 - SPARQLtoText version

Table of Contents

Dataset Description

Dataset Summary

Special version of LC-QuAD 2.0 for the SPARQL-to-Text task

New field simplified_query

New field is named "simplified_query". It results from applying the following step on the field "query":

  • Replacing URIs with a simpler format with prefix "resource:", "property:" and "ontology:".

  • Spacing the delimiters (, {, ., }, ).

  • Adding diversity to some filters which test a number (contains ( ?var, 'number' ) can become contains ?var = number

  • Randomizing the variables names

  • Shuffling the clauses

New split "valid"

A validation set was randonly extracted from the test set to represent 10% of the whole dataset.

Supported tasks

  • Knowledge-based question-answering
  • Text-to-SPARQL conversion
  • SPARQL-to-Text conversion

Languages

  • English

Dataset Structure

The corpus follows the global architecture from the original version of CSQA (https://amritasaha1812.github.io/CSQA/).

There is one directory of the train, dev, and test sets, respectively.

Dialogues are stored in separate directories, 100 dialogues per directory.

Finally, each dialogue is stored in a JSON file as a list of turns.

Types of questions

Comparison of question types compared to related datasets:

SimpleQuestions ParaQA LC-QuAD 2.0 CSQA WebNLQ-QA
Number of triplets in query 1 ✓ ✓ ✓ ✓ ✓
2 ✓ ✓ ✓ ✓
More ✓ ✓ ✓
Logical connector between triplets Conjunction ✓ ✓ ✓ ✓ ✓
Disjunction ✓ ✓
Exclusion ✓ ✓
Topology of the query graph Direct ✓ ✓ ✓ ✓ ✓
Sibling ✓ ✓ ✓ ✓
Chain ✓ ✓ ✓ ✓
Mixed ✓ ✓
Other ✓ ✓ ✓ ✓
Variable typing in the query None ✓ ✓ ✓ ✓ ✓
Target variable ✓ ✓ ✓ ✓
Internal variable ✓ ✓ ✓ ✓
Comparisons clauses None ✓ ✓ ✓ ✓ ✓
String ✓ ✓
Number ✓ ✓ ✓
Date ✓ ✓
Superlative clauses No ✓ ✓ ✓ ✓ ✓
Yes ✓
Answer type Entity (open) ✓ ✓ ✓ ✓ ✓
Entity (closed) ✓ ✓
Number ✓ ✓ ✓
Boolean ✓ ✓ ✓ ✓
Answer cardinality 0 (unanswerable) ✓ ✓
1 ✓ ✓ ✓ ✓ ✓
More ✓ ✓ ✓ ✓
Number of target variables 0 (⇒ ASK verb) ✓ ✓ ✓ ✓
1 ✓ ✓ ✓ ✓ ✓
2 ✓ ✓
Dialogue context Self-sufficient ✓ ✓ ✓ ✓ ✓
Coreference ✓ ✓
Ellipsis ✓ ✓
Meaning Meaningful ✓ ✓ ✓ ✓ ✓
Non-sense ✓

Data splits

Text verbalization is only available for a subset of the test set, referred to as challenge set. Other sample only contain dialogues in the form of follow-up sparql queries.

Train Validation Test
Questions 21,000 3,000 6,000
NL question per query 1
Characters per query 108 (± 36)
Tokens per question 10.6 (± 3.9)

Additional information

Related datasets

This corpus is part of a set of 5 datasets released for SPARQL-to-Text generation, namely:

Licencing information

  • Content from original dataset: CC-BY 3.0
  • New content: CC BY-SA 4.0

Citation information

This version of the corpus (with normalized SPARQL queries) 

@inproceedings{lecorve2022sparql2text,
  title={SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications},
  author={Lecorv\'e, Gw\'enol\'e and Veyret, Morgan and Brabant, Quentin and Rojas-Barahona, Lina M.},
  journal={Proceedings of the Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing (AACL-IJCNLP)},
  year={2022}
}

Original version 

@inproceedings{dubey2017lc2,
   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)},
   year={2019},
   organization={Springer}
}