Orange/lc_quad2-sparqltotext

Dataset Card for LC-QuAD 2.0 - SPARQLtoText version

Table of Contents

• —Dataset Card for LC-QuAD 2.0 - SPARQLtoText version
• —Table of Contents
• —Dataset Description
• —Dataset Summary
• —New field `simplified_query`
• —New split "valid"
• —Supported tasks
• —Languages
• —Dataset Structure
• —Types of questions
• —Data splits
• —Additional information
• —Related datasets
• —Licencing information
• —Citation information
• —This version of the corpus (with normalized SPARQL queries)
• —Original version

Dataset Description

• —Paper: SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications (AACL-IJCNLP 2022)
• —Point of Contact: Gwénolé Lecorvé

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:

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.

Additional information

Related datasets

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

• —Non conversational datasets
• —SimpleQuestions (from https://github.com/askplatypus/wikidata-simplequestions)
• —ParaQA (from https://github.com/barshana-banerjee/ParaQA)
• —LC-QuAD 2.0 (from http://lc-quad.sda.tech/)
• —Conversational datasets
• —CSQA (from https://amritasaha1812.github.io/CSQA/)
• —WebNLQ-QA (derived from https://gitlab.com/shimorina/webnlg-dataset/-/tree/master/release_v3.0)

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}
}