Orange/lc_quad2-sparqltotext

Dataset Card for LC-QuAD 2.0 - SPARQLtoText version

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:

• 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}
}