README.md

---
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 Card for LC-QuAD 2.0 - SPARQLtoText version](#dataset-card-for-lc-quad-20---sparqltotext-version)
  - [Table of Contents](#table-of-contents)
  - [Dataset Description](#dataset-description)
    - [Dataset Summary](#dataset-summary)
      - [New field `simplified_query`](#new-field-simplified_query)
      - [New split "valid"](#new-split-valid)
    - [Supported tasks](#supported-tasks)
    - [Languages](#languages)
  - [Dataset Structure](#dataset-structure)
    - [Types of questions](#types-of-questions)
    - [Data splits](#data-splits)
  - [Additional information](#additional-information)
    - [Related datasets](#related-datasets)
    - [Licencing information](#licencing-information)
    - [Citation information](#citation-information)
      - [This version of the corpus (with normalized SPARQL queries)](#this-version-of-the-corpus-with-normalized-sparql-queries)
      - [Original version](#original-version)


## Dataset Description

- **Paper:** [SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications (AACL-IJCNLP 2022)](https://aclanthology.org/2022.aacl-main.11/)
- **Point of Contact:** Gwénolé Lecorvé

### Dataset Summary

Special version of [LC-QuAD 2.0](https://huggingface.co./datasets/lc_quad) 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](https://huggingface.co./datasets/OrangeInnov/simplequestions-sparqltotext) | [ParaQA](https://huggingface.co./datasets/OrangeInnov/paraqa-sparqltotext) | [LC-QuAD 2.0](https://huggingface.co./datasets/OrangeInnov/lcquad_2.0-sparqltotext) | [CSQA](https://huggingface.co./datasets/OrangeInnov/csqa-sparqltotext) | [WebNLQ-QA](https://huggingface.co./datasets/OrangeInnov/webnlg-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](https://huggingface.co./datasets/OrangeInnov/simplequestions-sparqltotext) (from https://github.com/askplatypus/wikidata-simplequestions)
    - [ParaQA](https://huggingface.co./datasets/OrangeInnov/paraqa-sparqltotext) (from https://github.com/barshana-banerjee/ParaQA)
    - [LC-QuAD 2.0](https://huggingface.co./datasets/OrangeInnov/lcquad_2.0-sparqltotext) (from http://lc-quad.sda.tech/)
  - Conversational datasets
    - [CSQA](https://huggingface.co./datasets/OrangeInnov/csqa-sparqltotext) (from https://amritasaha1812.github.io/CSQA/)
    - [WebNLQ-QA](https://huggingface.co./datasets/OrangeInnov/webnlg-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)

```bibtex
@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

```bibtex
@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}
}
```