Datasets:

DFKI-SLT
/

knowledge_net

Tasks:

Text Classification

Sub-tasks:

multi-class-classification

entity-linking-classification

Languages:

English

Size:

10K<n<100K

Tags:

knowledgenet

Dataset card Files Files and versions

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Dataset Card for "KnowledgeNet"

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

More Information Needed

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:

{
  "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:

{
  "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:

{
  "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.

{"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.

{"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.

{"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.

{"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.

{"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.

{"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

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed are labeled as no_relation. More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

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",}