# 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.
task_categories:text-classification task_ids:multi-class-classification task_ids:entity-linking-classification annotations_creators:expert-generated language_creators:found multilinguality:monolingual size_categories:10K<n<100K language:en knowledgenet region:us
{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": [], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification", "entity-linking-classification"], "pretty_name": "KnowledgeNet is a dataset for automatically populating a knowledge base", "tags": ["knowledgenet"], "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}]}
TAGS
#task_categories-text-classification #task_ids-multi-class-classification #task_ids-entity-linking-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #language-English #knowledgenet #region-us 

# Dataset Card for "KnowledgeNet"
## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions
## Dataset Description
- Repository: knowledge-net
- Paper: KnowledgeNet: A Benchmark Dataset for Knowledge Base Population
- 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.

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


### 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:


#### 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:


#### 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:

### 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.



- '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.



- 'objectUri': the Wikidata URI of the object mention, a 'string' feature.
- 'relation': the relation label of this instance, a 'string' classification label.



#### 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.




- '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.



- 'obj_uri': the Wikidata URI of the object mention, a 'string' feature.
- 'relation': the relation label of this instance, a 'string' classification label.




### Data Splits

## Dataset Creation
### Curation Rationale

### Source Data
#### Initial Data Collection and Normalization

#### Who are the source language producers?

### Annotations
#### Annotation process

are labeled as no_relation.

### Personal and Sensitive Information

## Considerations for Using the Data
### Social Impact of Dataset

### Discussion of Biases

### Other Known Limitations

## Additional Information
### Dataset Curators

### Licensing Information




### Contributions
Thanks to @phucdev for adding this dataset.
# Dataset Card for "KnowledgeNet" ## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions ## Dataset Description
- Repository: knowledge-net
- Paper: KnowledgeNet: A Benchmark Dataset for Knowledge Base Population
- 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.

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 ### 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: #### 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: #### 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: ### 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.



- '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.



- 'objectUri': the Wikidata URI of the object mention, a 'string' feature.
- 'relation': the relation label of this instance, a 'string' classification label. #### 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.




- '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.



- 'obj_uri': the Wikidata URI of the object mention, a 'string' feature.
- 'relation': the relation label of this instance, a 'string' classification label. ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process

are labeled as no_relation. ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions
Thanks to @phucdev for adding this dataset.
TAGS
#task_categories-text-classification #task_ids-multi-class-classification #task_ids-entity-linking-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #language-English #knowledgenet #region-us 
 # Dataset Card for "KnowledgeNet" ## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions ## Dataset Description
- Repository: knowledge-net
- Paper: KnowledgeNet: A Benchmark Dataset for Knowledge Base Population
- 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.

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 ### 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: #### 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: #### 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: ### 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.



- '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.



- 'objectUri': the Wikidata URI of the object mention, a 'string' feature.
- 'relation': the relation label of this instance, a 'string' classification label. #### 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.




- '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.



- 'obj_uri': the Wikidata URI of the object mention, a 'string' feature.
- 'relation': the relation label of this instance, a 'string' classification label. ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process

are labeled as no_relation. ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions
Thanks to @phucdev for adding this dataset.