Datasets:

drt
/

kqa_pro

	---
	annotations_creators:
	- machine-generated
	- expert-generated
	language:
	- en
	language_creators:
	- found
	license:
	- mit
	multilinguality:
	- monolingual
	pretty_name: KQA-Pro
	size_categories:
	- 10K<n<100K
	source_datasets:
	- original
	tags:
	- knowledge graph
	- freebase
	task_categories:
	- question-answering
	task_ids:
	- open-domain-qa
	---

	# Dataset Card for KQA Pro

	## Table of Contents
	- [Table of Contents](#table-of-contents)
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Configs](#data-configs)
	- [Data Splits](#data-splits)
	- [Additional Information](#additional-information)
	- [How to run SPARQLs and programs](#how-to-run-sparqls-and-programs)
	- [Knowledge Graph File](#knowledge-graph-file)
	- [How to Submit to Leaderboard](#how-to-submit-results-of-test-set)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## Dataset Description

	- Homepage: http://thukeg.gitee.io/kqa-pro/
	- Repository: https://github.com/shijx12/KQAPro_Baselines
	- Paper: [KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base](https://aclanthology.org/2022.acl-long.422/)
	- Leaderboard: http://thukeg.gitee.io/kqa-pro/leaderboard.html
	- Point of Contact: shijx12 at gmail dot com

	### Dataset Summary

	KQA Pro is a large-scale dataset of complex question answering over knowledge base. The questions are very diverse and challenging, requiring multiple reasoning capabilities including compositional reasoning, multi-hop reasoning, quantitative comparison, set operations, and etc. Strong supervisions of SPARQL and program are provided for each question.

	### Supported Tasks and Leaderboards

	It supports knowlege graph based question answering. Specifically, it provides SPARQL and program for each question.

	### Languages

	English

	## Dataset Structure

	train.json/val.json
	```
	[
	{
	'question': str,
	'sparql': str, # executable in our virtuoso engine
	'program':
	[
	{
	'function': str, # function name
	'dependencies': [int], # functional inputs, representing indices of the preceding functions
	'inputs': [str], # textual inputs
	}
	],
	'choices': [str], # 10 answer choices
	'answer': str, # golden answer
	}
	]
	```

	test.json
	```
	[
	{
	'question': str,
	'choices': [str], # 10 answer choices
	}
	]
	```

	### Data Configs

	This dataset has two configs: `train_val` and `test` because they have different available fields. Please specify this like `load_dataset('drt/kqa_pro', 'train_val')`.

	### Data Splits

	train, val, test


	## Additional Information

	### Knowledge Graph File

	You can find the knowledge graph file `kb.json` in the original github repository. It comes with the format:

	```json
	{
	'concepts':
	{
	'<id>':
	{
	'name': str,
	'instanceOf': ['<id>', '<id>'], # ids of parent concept
	}
	},
	'entities': # excluding concepts
	{
	'<id>':
	{
	'name': str,
	'instanceOf': ['<id>', '<id>'], # ids of parent concept
	'attributes':
	[
	{
	'key': str, # attribute key
	'value': # attribute value
	{
	'type': 'string'/'quantity'/'date'/'year',
	'value': float/int/str, # float or int for quantity, int for year, 'yyyy/mm/dd' for date
	'unit': str, # for quantity
	},
	'qualifiers':
	{
	'<qk>': # qualifier key, one key may have multiple corresponding qualifier values
	[
	{
	'type': 'string'/'quantity'/'date'/'year',
	'value': float/int/str,
	'unit': str,
	}, # the format of qualifier value is similar to attribute value
	]
	}
	},
	]
	'relations':
	[
	{
	'predicate': str,
	'object': '<id>', # NOTE: it may be a concept id
	'direction': 'forward'/'backward',
	'qualifiers':
	{
	'<qk>': # qualifier key, one key may have multiple corresponding qualifier values
	[
	{
	'type': 'string'/'quantity'/'date'/'year',
	'value': float/int/str,
	'unit': str,
	}, # the format of qualifier value is similar to attribute value
	]
	}
	},
	]
	}
	}
	}
	```



	### How to run SPARQLs and programs

	We implement multiple baselines in our [codebase](https://github.com/shijx12/KQAPro_Baselines), which includes a supervised SPARQL parser and program parser.

	In the SPARQL parser, we implement a query engine based on [Virtuoso](https://github.com/openlink/virtuoso-opensource.git).
	You can install the engine based on our [instructions](https://github.com/shijx12/KQAPro_Baselines/blob/master/SPARQL/README.md), and then feed your predicted SPARQL to get the answer.

	In the program parser, we implement a rule-based program executor, which receives a predicted program and returns the answer.
	Detailed introductions of our functions can be found in our [paper](https://arxiv.org/abs/2007.03875).

	### How to submit results of test set
	You need to predict answers for all questions of test set and write them in a text file in order, one per line.
	Here is an example:
	```
	Tron: Legacy
	Palm Beach County
	1937-03-01
	The Queen
	...
	```

	Then you need to send the prediction file to us by email <caosl19@mails.tsinghua.edu.cn>, we will reply to you with the performance as soon as possible.
	To appear in the learderboard, you need to also provide following information:

	- model name
	- affiliation
	- open-ended or multiple-choice
	- whether use the supervision of SPARQL in your model or not
	- whether use the supervision of program in your model or not
	- single model or ensemble model
	- (optional) paper link
	- (optional) code link

	### Licensing Information

	MIT License

	### Citation Information

	If you find our dataset is helpful in your work, please cite us by

	```
	@inproceedings{KQAPro,
	title={{KQA P}ro: A Large Diagnostic Dataset for Complex Question Answering over Knowledge Base},
	author={Cao, Shulin and Shi, Jiaxin and Pan, Liangming and Nie, Lunyiu and Xiang, Yutong and Hou, Lei and Li, Juanzi and He, Bin and Zhang, Hanwang},
	booktitle={ACL'22},
	year={2022}
	}
	```

	### Contributions

	Thanks to [@happen2me](https://github.com/happen2me) for adding this dataset.