Datasets:
Tasks:
Text Classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
expert-generated
File size: 20,850 Bytes
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---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license: []
multilinguality:
- monolingual
pretty_name: KnowledgeNet is a dataset for automatically populating a knowledge base
size_categories:
- 10K<n<100K
source_datasets: []
tags:
- knowledgenet
task_categories:
- text-classification
task_ids:
- multi-class-classification
- entity-linking-classification
dataset_info:
- config_name: knet
features:
- name: fold
dtype: int32
- name: documentId
dtype: string
- name: source
dtype: string
- name: documentText
dtype: string
- name: passages
sequence:
- name: passageId
dtype: string
- name: passageStart
dtype: int32
- name: passageEnd
dtype: int32
- name: passageText
dtype: string
- name: exhaustivelyAnnotatedProperties
sequence:
- name: propertyId
dtype: string
- name: propertyName
dtype: string
- name: propertyDescription
dtype: string
- name: facts
sequence:
- name: factId
dtype: string
- name: propertyId
dtype: string
- name: humanReadable
dtype: string
- name: annotatedPassage
dtype: string
- name: subjectStart
dtype: int32
- name: subjectEnd
dtype: int32
- name: subjectText
dtype: string
- name: subjectUri
dtype: string
- name: objectStart
dtype: int32
- name: objectEnd
dtype: int32
- name: objectText
dtype: string
- name: objectUri
dtype: string
splits:
- name: train
num_bytes: 10161415
num_examples: 3977
download_size: 14119313
dataset_size: 10161415
- config_name: knet_tokenized
features:
- name: doc_id
dtype: string
- name: passage_id
dtype: string
- name: fact_id
dtype: string
- name: tokens
sequence: string
- name: subj_start
dtype: int32
- name: subj_end
dtype: int32
- name: subj_type
dtype:
class_label:
names:
'0': O
'1': PER
'2': ORG
'3': LOC
'4': DATE
- name: subj_uri
dtype: string
- name: obj_start
dtype: int32
- name: obj_end
dtype: int32
- name: obj_type
dtype:
class_label:
names:
'0': O
'1': PER
'2': ORG
'3': LOC
'4': DATE
- name: obj_uri
dtype: string
- name: relation
dtype:
class_label:
names:
'0': NO_RELATION
'1': DATE_OF_BIRTH
'2': DATE_OF_DEATH
'3': PLACE_OF_RESIDENCE
'4': PLACE_OF_BIRTH
'5': NATIONALITY
'6': EMPLOYEE_OR_MEMBER_OF
'7': EDUCATED_AT
'8': POLITICAL_AFFILIATION
'9': CHILD_OF
'10': SPOUSE
'11': DATE_FOUNDED
'12': HEADQUARTERS
'13': SUBSIDIARY_OF
'14': FOUNDED_BY
'15': CEO
splits:
- name: train
num_bytes: 4511963
num_examples: 10895
download_size: 14119313
dataset_size: 4511963
- config_name: knet_re
features:
- name: documentId
dtype: string
- name: passageId
dtype: string
- name: factId
dtype: string
- name: passageText
dtype: string
- name: humanReadable
dtype: string
- name: annotatedPassage
dtype: string
- name: subjectStart
dtype: int32
- name: subjectEnd
dtype: int32
- name: subjectText
dtype: string
- name: subjectType
dtype:
class_label:
names:
'0': O
'1': PER
'2': ORG
'3': LOC
'4': DATE
- name: subjectUri
dtype: string
- name: objectStart
dtype: int32
- name: objectEnd
dtype: int32
- name: objectText
dtype: string
- name: objectType
dtype:
class_label:
names:
'0': O
'1': PER
'2': ORG
'3': LOC
'4': DATE
- name: objectUri
dtype: string
- name: relation
dtype:
class_label:
names:
'0': NO_RELATION
'1': DATE_OF_BIRTH
'2': DATE_OF_DEATH
'3': PLACE_OF_RESIDENCE
'4': PLACE_OF_BIRTH
'5': NATIONALITY
'6': EMPLOYEE_OR_MEMBER_OF
'7': EDUCATED_AT
'8': POLITICAL_AFFILIATION
'9': CHILD_OF
'10': SPOUSE
'11': DATE_FOUNDED
'12': HEADQUARTERS
'13': SUBSIDIARY_OF
'14': FOUNDED_BY
'15': CEO
splits:
- name: train
num_bytes: 6098219
num_examples: 10895
download_size: 14119313
dataset_size: 6098219
---
# Dataset Card for "KnowledgeNet"
## 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 Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [knowledge-net](https://github.com/diffbot/knowledge-net)
- **Paper:** [KnowledgeNet: A Benchmark Dataset for Knowledge Base Population](https://aclanthology.org/D19-1069/)
- **Size of downloaded dataset files:** 12.59 MB
- **Size of the generated dataset:** 6.1 MB
### Dataset Summary
KnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge base (Wikidata) with facts
expressed in natural language text on the web. KnowledgeNet provides text exhaustively annotated with facts, thus
enabling the holistic end-to-end evaluation of knowledge base population systems as a whole, unlike previous benchmarks
that are more suitable for the evaluation of individual subcomponents (e.g., entity linking, relation extraction).
For instance, the dataset contains text expressing the fact (Gennaro Basile; RESIDENCE; Moravia), in the passage:
"Gennaro Basile was an Italian painter, born in Naples but active in the German-speaking countries. He settled at Brünn,
in Moravia, and lived about 1756..."
For a description of the dataset and baseline systems, please refer to their
[EMNLP paper](https://github.com/diffbot/knowledge-net/blob/master/knowledgenet-emnlp-cameraready.pdf).
Note: This Datasetreader currently only supports the `train` split and does not contain negative examples.
In addition to the original format this repository also provides two version (`knet_re`, `knet_tokenized`) that are
easier to use for simple relation extraction. You can load them with
`datasets.load_dataset("DFKI-SLT/knowledge_net", name="<config>")`.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
The language in the dataset is English.
## Dataset Structure
### Data Instances
#### knet
- **Size of downloaded dataset files:** 12.59 MB
- **Size of the generated dataset:** 10.16 MB
An example of 'train' looks as follows:
```json
{
"fold": 2,
"documentId": "8313",
"source": "DBpedia Abstract",
"documentText": "Gennaro Basile\n\nGennaro Basile was an Italian painter, born in Naples but active in the German-speaking countries. He settled at Brünn, in Moravia, and lived about 1756. His best picture is the altar-piece in the chapel of the chateau at Seeberg, in Salzburg. Most of his works remained in Moravia.",
"passages": [
{
"passageId": "8313:16:114",
"passageStart": 16,
"passageEnd": 114,
"passageText": "Gennaro Basile was an Italian painter, born in Naples but active in the German-speaking countries.",
"exhaustivelyAnnotatedProperties": [
{
"propertyId": "12",
"propertyName": "PLACE_OF_BIRTH",
"propertyDescription": "Describes the relationship between a person and the location where she/he was born."
}
],
"facts": [
{
"factId": "8313:16:30:63:69:12",
"propertyId": "12",
"humanReadable": "<Gennaro Basile> <PLACE_OF_BIRTH> <Naples>",
"annotatedPassage": "<Gennaro Basile> was an Italian painter, born in <Naples> but active in the German-speaking countries.",
"subjectStart": 16,
"subjectEnd": 30,
"subjectText": "Gennaro Basile",
"subjectUri": "http://www.wikidata.org/entity/Q19517888",
"objectStart": 63,
"objectEnd": 69,
"objectText": "Naples",
"objectUri": "http://www.wikidata.org/entity/Q2634"
}
]
},
{
"passageId": "8313:115:169",
"passageStart": 115,
"passageEnd": 169,
"passageText": "He settled at Brünn, in Moravia, and lived about 1756.",
"exhaustivelyAnnotatedProperties": [
{
"propertyId": "11",
"propertyName": "PLACE_OF_RESIDENCE",
"propertyDescription": "Describes the relationship between a person and the location where she/he lives/lived."
},
{
"propertyId": "12",
"propertyName": "PLACE_OF_BIRTH",
"propertyDescription": "Describes the relationship between a person and the location where she/he was born."
}
],
"facts": [
{
"factId": "8313:115:117:129:134:11",
"propertyId": "11",
"humanReadable": "<He> <PLACE_OF_RESIDENCE> <Brünn>",
"annotatedPassage": "<He> settled at <Brünn>, in Moravia, and lived about 1756.",
"subjectStart": 115,
"subjectEnd": 117,
"subjectText": "He",
"subjectUri": "http://www.wikidata.org/entity/Q19517888",
"objectStart": 129,
"objectEnd": 134,
"objectText": "Brünn",
"objectUri": "http://www.wikidata.org/entity/Q14960"
},
{
"factId": "8313:115:117:139:146:11",
"propertyId": "11",
"humanReadable": "<He> <PLACE_OF_RESIDENCE> <Moravia>",
"annotatedPassage": "<He> settled at Brünn, in <Moravia>, and lived about 1756.",
"subjectStart": 115,
"subjectEnd": 117,
"subjectText": "He",
"subjectUri": "http://www.wikidata.org/entity/Q19517888",
"objectStart": 139,
"objectEnd": 146,
"objectText": "Moravia",
"objectUri": "http://www.wikidata.org/entity/Q43266"
}
]
}
]
}
```
#### knet_re
- **Size of downloaded dataset files:** 12.59 MB
- **Size of the generated dataset:** 6.1 MB
An example of 'train' looks as follows:
```json
{
"documentId": "7",
"passageId": "7:23:206",
"factId": "7:23:44:138:160:1",
"passageText": "Tata Chemicals Europe (formerly Brunner Mond (UK) Limited) is a UK-based chemicals company that is a subsidiary of Tata Chemicals Limited, itself a part of the India-based Tata Group.",
"humanReadable": "<Tata Chemicals Europe> <SUBSIDIARY_OF> <Tata Chemicals Limited>",
"annotatedPassage": "<Tata Chemicals Europe> (formerly Brunner Mond (UK) Limited) is a UK-based chemicals company that is a subsidiary of <Tata Chemicals Limited>, itself a part of the India-based Tata Group.",
"subjectStart": 0,
"subjectEnd": 21,
"subjectText": "Tata Chemicals Europe",
"subjectType": 2,
"subjectUri": "",
"objectStart": 115,
"objectEnd": 137,
"objectText": "Tata Chemicals Limited",
"objectType": 2,
"objectUri": "http://www.wikidata.org/entity/Q2331365",
"relation": 13
}
```
#### knet_tokenized
- **Size of downloaded dataset files:** 12.59 MB
- **Size of the generated dataset:** 4.5 MB
An example of 'train' looks as follows:
```json
{
"doc_id": "7",
"passage_id": "7:23:206",
"fact_id": "7:162:168:183:205:1",
"tokens": ["Tata", "Chemicals", "Europe", "(", "formerly", "Brunner", "Mond", "(", "UK", ")", "Limited", ")", "is", "a", "UK", "-", "based", "chemicals", "company", "that", "is", "a", "subsidiary", "of", "Tata", "Chemicals", "Limited", ",", "itself", "a", "part", "of", "the", "India", "-", "based", "Tata", "Group", "."],
"subj_start": 28,
"subj_end": 29,
"subj_type": 2,
"subj_uri": "http://www.wikidata.org/entity/Q2331365",
"obj_start": 33,
"obj_end": 38,
"obj_type": 2,
"obj_uri": "http://www.wikidata.org/entity/Q331715",
"relation": 13
}
```
### Data Fields
#### knet
- `fold`: the fold, a `int` feature.
- `documentId`: the document id, a `string` feature.
- `source`: the source, a `string` feature.
- `documenText`: the document text, a `string` feature.
- `passages`: the list of passages, a `list` of `dict`.
- `passageId`: the passage id, a `string` feature.
- `passageStart`: the passage start, a `int` feature.
- `passageEnd`: the passage end, a `int` feature.
- `passageText`: the passage text, a `string` feature.
- `exhaustivelyAnnotatedProperties`: the list of exhaustively annotated properties, a `list` of `dict`.
- `propertyId`: the property id, a `string` feature.
- `propertyName`: the property name, a `string` feature.
- `propertyDescription`: the property description, a `string` feature.
- `facts`: the list of facts, a `list` of `dict`.
- `factId`: the fact id, a `string` feature.
- `propertyId`: the property id, a `string` feature.
- `humanReadable`: the human readable annotation, a `string` feature.
- `annotatedPassage`: the annotated passage, a `string` feature.
- `subjectStart`: the subject start, a `int` feature.
- `subjectEnd`: the subject end, a `int` feature.
- `subjectText`: the subject text, a `string` feature.
- `subjectUri`: the subject uri, a `string` feature.
- `objectStart`: the object start, a `int` feature.
- `objectEnd`: the object end, a `int` feature.
- `objectText`: the object text, a `string` feature.
- `objectUri`: the object uri, a `string` feature.
#### knet_re
- `documentId`: the document id, a `string` feature.
- `passageId`: the passage id, a `string` feature.
- `passageText`: the passage text, a `string` feature.
- `factId`: the fact id, a `string` feature.
- `humanReadable`: human-readable annotation, a `string` features.
- `annotatedPassage`: annotated passage, a `string` feature.
- `subjectStart`: the index of the start character of the relation subject mention, an `ìnt` feature.
- `subjectEnd`: the index of the end character of the relation subject mention, exclusive, an `ìnt` feature.
- `subjectText`: the text the subject mention, a `string` feature.
- `subjectType`: the NER type of the subject mention, a `string` classification label.
```json
{"O": 0, "PER": 1, "ORG": 2, "LOC": 3, "DATE": 4}
```
- `subjectUri`: the Wikidata URI of the subject mention, a `string` feature.
- `objectStart`: the index of the start character of the relation object mention, an `ìnt` feature.
- `objectEnd`: the index of the end character of the relation object mention, exclusive, an `ìnt` feature.
- `objectText`: the text the object mention, a `string` feature.
- `objectType`: the NER type of the object mention, a `string` classification label.
```json
{"O": 0, "PER": 1, "ORG": 2, "LOC": 3, "DATE": 4}
```
- `objectUri`: the Wikidata URI of the object mention, a `string` feature.
- `relation`: the relation label of this instance, a `string` classification label.
```json
{"NO_RELATION": 0, "DATE_OF_BIRTH": 1, "DATE_OF_DEATH": 2, "PLACE_OF_RESIDENCE": 3, "PLACE_OF_BIRTH": 4, "NATIONALITY": 5, "EMPLOYEE_OR_MEMBER_OF": 6, "EDUCATED_AT": 7, "POLITICAL_AFFILIATION": 8, "CHILD_OF": 9, "SPOUSE": 10, "DATE_FOUNDED": 11, "HEADQUARTERS": 12, "SUBSIDIARY_OF": 13, "FOUNDED_BY": 14, "CEO": 15}
```
#### knet_tokenized
- `doc_id`: the document id, a `string` feature.
- `passage_id`: the passage id, a `string` feature.
- `factId`: the fact id, a `string` feature.
- `tokens`: the list of tokens of this passage, obtained with spaCy, a `list` of `string` features.
- `subj_start`: the index of the start token of the relation subject mention, an `ìnt` feature.
- `subj_end`: the index of the end token of the relation subject mention, exclusive, an `ìnt` feature.
- `subj_type`: the NER type of the subject mention, a `string` classification label.
```json
{"O": 0, "PER": 1, "ORG": 2, "LOC": 3, "DATE": 4}
```
- `subj_uri`: the Wikidata URI of the subject mention, a `string` feature.
- `obj_start`: the index of the start token of the relation object mention, an `ìnt` feature.
- `obj_end`: the index of the end token of the relation object mention, exclusive, an `ìnt` feature.
- `obj_type`: the NER type of the object mention, a `string` classification label.
```json
{"O": 0, "PER": 1, "ORG": 2, "LOC": 3, "DATE": 4}
```
- `obj_uri`: the Wikidata URI of the object mention, a `string` feature.
- `relation`: the relation label of this instance, a `string` classification label.
```json
{"NO_RELATION": 0, "DATE_OF_BIRTH": 1, "DATE_OF_DEATH": 2, "PLACE_OF_RESIDENCE": 3, "PLACE_OF_BIRTH": 4, "NATIONALITY": 5, "EMPLOYEE_OR_MEMBER_OF": 6, "EDUCATED_AT": 7, "POLITICAL_AFFILIATION": 8, "CHILD_OF": 9, "SPOUSE": 10, "DATE_FOUNDED": 11, "HEADQUARTERS": 12, "SUBSIDIARY_OF": 13, "FOUNDED_BY": 14, "CEO": 15}
```
### Data Splits
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
are labeled as no_relation.
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@inproceedings{mesquita-etal-2019-knowledgenet,
title = "{K}nowledge{N}et: A Benchmark Dataset for Knowledge Base Population",
author = "Mesquita, Filipe and
Cannaviccio, Matteo and
Schmidek, Jordan and
Mirza, Paramita and
Barbosa, Denilson",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1069",
doi = "10.18653/v1/D19-1069",
pages = "749--758",}
```
### Contributions
Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset. |