Upload dataloading script and README.md

README.md

@@ -0,0 +1,565 @@
+---
+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.

knowledge_net.py

@@ -0,0 +1,368 @@
+# coding=utf-8
+# Copyright 2022 The current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""The KnowledgeNet dataset for automatically populating a knowledge base"""
+
+import json
+import re
+import datasets
+
+_CITATION = """\
+@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",}
+"""
+
+_DESCRIPTION = """\
+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
+"""
+
+_HOMEPAGE = "https://github.com/diffbot/knowledge-net"
+
+_LICENSE = ""
+
+# The HuggingFace dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    "train": "https://raw.githubusercontent.com/diffbot/knowledge-net/master/dataset/train.json",
+    "test": "https://raw.githubusercontent.com/diffbot/knowledge-net/master/dataset/test-no-facts.json"
+}
+
+_VERSION = datasets.Version("1.1.0")
+
+_CLASS_LABELS = [
+    "NO_RELATION",
+    "DATE_OF_BIRTH",
+    "DATE_OF_DEATH",
+    "PLACE_OF_RESIDENCE",
+    "PLACE_OF_BIRTH",
+    "NATIONALITY",
+    "EMPLOYEE_OR_MEMBER_OF",
+    "EDUCATED_AT",
+    "POLITICAL_AFFILIATION",
+    "CHILD_OF",
+    "SPOUSE",
+    "DATE_FOUNDED",
+    "HEADQUARTERS",
+    "SUBSIDIARY_OF",
+    "FOUNDED_BY",
+    "CEO"
+]
+
+_NER_CLASS_LABELS = [
+    "O",
+    "PER",
+    "ORG",
+    "LOC",
+    "DATE"
+]
+
+
+def get_entity_types_from_relation(relation_label):
+    if relation_label == "DATE_OF_BIRTH":
+        subj_type = "PER"
+        obj_type = "DATE"
+    elif relation_label == "DATE_OF_DEATH":
+        subj_type = "PER"
+        obj_type = "DATE"
+    elif relation_label == "PLACE_OF_RESIDENCE":
+        subj_type = "PER"
+        obj_type = "LOC"
+    elif relation_label == "PLACE_OF_BIRTH":
+        subj_type = "PER"
+        obj_type = "LOC"
+    elif relation_label == "NATIONALITY":
+        subj_type = "PER"
+        obj_type = "LOC"
+    elif relation_label == "EMPLOYEE_OR_MEMBER_OF":
+        subj_type = "PER"
+        obj_type = "ORG"
+    elif relation_label == "EDUCATED_AT":
+        subj_type = "PER"
+        obj_type = "ORG"
+    elif relation_label == "POLITICAL_AFFILIATION":
+        subj_type = "PER"
+        obj_type = "ORG"
+    elif relation_label == "CHILD_OF":
+        subj_type = "PER"
+        obj_type = "PER"
+    elif relation_label == "SPOUSE":
+        subj_type = "PER"
+        obj_type = "PER"
+    elif relation_label == "DATE_FOUNDED":
+        subj_type = "ORG"
+        obj_type = "DATE"
+    elif relation_label == "HEADQUARTERS":
+        subj_type = "ORG"
+        obj_type = "LOC"
+    elif relation_label == "SUBSIDIARY_OF":
+        subj_type = "ORG"
+        obj_type = "ORG"
+    elif relation_label == "FOUNDED_BY":
+        subj_type = "ORG"
+        obj_type = "PER"
+    elif relation_label == "CEO":
+        subj_type = "ORG"
+        obj_type = "PER"
+    else:
+        raise ValueError(f"Unknown relation label: {relation_label}")
+    return subj_type, obj_type
+
+
+def remove_contiguous_whitespaces(text):
+    # +1 to account for regular whitespace at the beginning
+    contiguous_whitespaces_indices = [(m.start(0) + 1, m.end(0)) for m in re.finditer('  +', text)]
+    cleaned_text = re.sub(" +", " ", text)
+    return cleaned_text, contiguous_whitespaces_indices
+
+
+def fix_char_index(char_index, contiguous_whitespaces_indices):
+    new_char_index = char_index
+    offset = 0
+    for ws_start, ws_end in contiguous_whitespaces_indices:
+        if char_index >= ws_end:
+            offset = offset + (ws_end - ws_start)
+    new_char_index -= offset
+    return new_char_index
+
+
+class KnowledgeNet(datasets.GeneratorBasedBuilder):
+    """The KnowledgeNet dataset for automatically populating a knowledge base"""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="knet", version=_VERSION, description="The original KnowledgeNet formatted for RE."
+        ),
+        datasets.BuilderConfig(
+            name="knet_re", version=_VERSION, description="The original KnowledgeNet formatted for RE."
+        ),
+        datasets.BuilderConfig(
+            name="knet_tokenized", version=_VERSION, description="KnowledgeNet tokenized and reformatted."
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "knet"  # type: ignore
+
+    def _info(self):
+        if self.config.name == "knet_tokenized":
+            features = datasets.Features(
+                {
+                    "doc_id": datasets.Value("string"),
+                    "passage_id": datasets.Value("string"),
+                    "fact_id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "subj_start": datasets.Value("int32"),
+                    "subj_end": datasets.Value("int32"),
+                    "subj_type": datasets.ClassLabel(names=_NER_CLASS_LABELS),
+                    "subj_uri": datasets.Value("string"),
+                    "obj_start": datasets.Value("int32"),
+                    "obj_end": datasets.Value("int32"),
+                    "obj_type": datasets.ClassLabel(names=_NER_CLASS_LABELS),
+                    "obj_uri": datasets.Value("string"),
+                    "relation": datasets.ClassLabel(names=_CLASS_LABELS),
+                }
+            )
+        elif self.config.name == "knet_re":
+            features = datasets.Features(
+                {
+                    "documentId": datasets.Value("string"),
+                    "passageId": datasets.Value("string"),
+                    "factId": datasets.Value("string"),
+                    "passageText": datasets.Value("string"),
+                    "humanReadable": datasets.Value("string"),
+                    "annotatedPassage": datasets.Value("string"),
+                    "subjectStart": datasets.Value("int32"),
+                    "subjectEnd": datasets.Value("int32"),
+                    "subjectText": datasets.Value("string"),
+                    "subjectType": datasets.ClassLabel(names=_NER_CLASS_LABELS),
+                    "subjectUri": datasets.Value("string"),
+                    "objectStart": datasets.Value("int32"),
+                    "objectEnd": datasets.Value("int32"),
+                    "objectText": datasets.Value("string"),
+                    "objectType": datasets.ClassLabel(names=_NER_CLASS_LABELS),
+                    "objectUri": datasets.Value("string"),
+                    "relation": datasets.ClassLabel(names=_CLASS_LABELS),
+                }
+            )
+        else:
+            features = datasets.Features(
+                {
+                    "fold": datasets.Value("int32"),
+                    "documentId": datasets.Value("string"),
+                    "source": datasets.Value("string"),
+                    "documentText": datasets.Value("string"),
+                    "passages": [{
+                            "passageId": datasets.Value("string"),
+                            "passageStart": datasets.Value("int32"),
+                            "passageEnd": datasets.Value("int32"),
+                            "passageText": datasets.Value("string"),
+                            "exhaustivelyAnnotatedProperties": [{
+                                    "propertyId": datasets.Value("string"),
+                                    "propertyName": datasets.Value("string"),
+                                    "propertyDescription": datasets.Value("string"),
+                            }],
+                            "facts": [{
+                                    "factId": datasets.Value("string"),
+                                    "propertyId": datasets.Value("string"),
+                                    "humanReadable": datasets.Value("string"),
+                                    "annotatedPassage": datasets.Value("string"),
+                                    "subjectStart": datasets.Value("int32"),
+                                    "subjectEnd": datasets.Value("int32"),
+                                    "subjectText": datasets.Value("string"),
+                                    "subjectUri": datasets.Value("string"),
+                                    "objectStart": datasets.Value("int32"),
+                                    "objectEnd": datasets.Value("int32"),
+                                    "objectText": datasets.Value("string"),
+                                    "objectUri": datasets.Value("string"),
+                            }],
+                    }],
+                }
+            )
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+        # splits = [datasets.Split.TRAIN, datasets.Split.TEST]
+        splits = [datasets.Split.TRAIN]
+        return [datasets.SplitGenerator(name=i, gen_kwargs={"filepath": downloaded_files[str(i)], "split": i})
+                for i in splits]
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples."""
+        # This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
+        # It is in charge of opening the given file and yielding (key, example) tuples from the dataset
+        # The key is not important, it's more here for legacy reason (legacy from tfds)
+        if self.config.name == "knet_tokenized":
+            from spacy.lang.en import English
+            word_splitter = English()
+        else:
+            word_splitter = None
+        with open(filepath, encoding="utf-8") as f:
+            for line in f:
+                doc = json.loads(line)
+                if self.config.name == "knet":
+                    yield doc["documentId"], doc
+                else:
+                    for passage in doc["passages"]:
+                        # Skip passages without facts right away
+                        if len(passage["facts"]) == 0:
+                            continue
+
+                        text = passage["passageText"]
+                        passage_start = passage["passageStart"]
+
+                        if self.config.name == "knet_tokenized":
+                            cleaned_text, contiguous_ws_indices = remove_contiguous_whitespaces(text)
+                            spacy_doc = word_splitter(cleaned_text)
+                            word_tokens = [t.text for t in spacy_doc]
+                            for fact in passage["facts"]:
+                                subj_start = fix_char_index(fact["subjectStart"] - passage_start, contiguous_ws_indices)
+                                subj_end = fix_char_index(fact["subjectEnd"] - passage_start, contiguous_ws_indices)
+                                obj_start = fix_char_index(fact["objectStart"] - passage_start, contiguous_ws_indices)
+                                obj_end = fix_char_index(fact["objectEnd"] - passage_start, contiguous_ws_indices)
+                                # Get exclusive token spans from char spans
+                                subj_span = spacy_doc.char_span(subj_start, subj_end, alignment_mode="expand")
+                                obj_span = spacy_doc.char_span(obj_start, obj_end, alignment_mode="expand")
+
+                                relation_label = fact["humanReadable"].split(">")[1][2:]
+                                subj_type, obj_type = get_entity_types_from_relation(relation_label)
+                                id_ = fact["factId"]
+
+                                yield id_, {
+                                    "doc_id": doc["documentId"],
+                                    "passage_id": passage["passageId"],
+                                    "fact_id": id_,
+                                    "tokens": word_tokens,
+                                    "subj_start": subj_span.start,
+                                    "subj_end": subj_span.end,
+                                    "subj_type": subj_type,
+                                    "subj_uri": fact["subjectUri"],
+                                    "obj_start": obj_span.start,
+                                    "obj_end": obj_span.end,
+                                    "obj_type": obj_type,
+                                    "obj_uri": fact["objectUri"],
+                                    "relation": relation_label
+                                }
+                        else:
+                            for fact in passage["facts"]:
+                                relation_label = fact["humanReadable"].split(">")[1][2:]
+                                subj_type, obj_type = get_entity_types_from_relation(relation_label)
+                                id_ = fact["factId"]
+                                yield id_, {
+                                    "documentId": doc["documentId"],
+                                    "passageId": passage["passageId"],
+                                    "passageText": passage["passageText"],
+                                    "factId": id_,
+                                    "humanReadable": fact["humanReadable"],
+                                    "annotatedPassage": fact["annotatedPassage"],
+                                    "subjectStart": fact["subjectStart"] - passage_start,
+                                    "subjectEnd": fact["subjectEnd"] - passage_start,
+                                    "subjectText": fact["subjectText"],
+                                    "subjectType": subj_type,
+                                    "subjectUri": fact["subjectUri"],
+                                    "objectStart": fact["objectStart"] - passage_start,
+                                    "objectEnd": fact["objectEnd"] - passage_start,
+                                    "objectText": fact["objectText"],
+                                    "objectType": obj_type,
+                                    "objectUri": fact["objectUri"],
+                                    "relation": relation_label
+                                }