Wikidata - All Entities

This Hugging Face Data Set contains the entirety of Wikidata as of the date listed below. Wikidata is a freely licensed structured knowledge graph following the wiki model of user contributions. If you build on this data please consider contributing back to Wikidata. For more on the size and other statistics of Wikidata, see: Special:Statistics. Current Dump as of: 2024-03-04

  Original Source

The data contained in this repository is retrieved… See the full description on the dataset page: https://huggingface.co/datasets/Wikimedians/wikidata-all.

A BitTorrent file to download data with the title 'wikidata-20220103-all.json.gz'

Persons of interest profiles from Wikidata, the structured data version of Wikipedia.

Regularly published dataset of PageRank scores for Wikidata entities. The underlying link graph is formed by a union of all links accross all Wikipedia language editions. Computation is performed Andreas Thalhammer with 'danker' available at https://github.com/athalhammer/danker . If you find the downloads here useful please feel free to leave a GitHub ⭐ at the repository and buy me a ☕ https://www.buymeacoffee.com/thalhamm

Category-based imports from Wikidata, the structured data version of Wikipedia.

derenrich/wikidata-en-descriptions-small dataset hosted on Hugging Face and contributed by the HF Datasets community

Wikidata dump retrieved from https://dumps.wikimedia.org/wikidatawiki/entities/latest-all.json.bz2 on 27 Dec 2017

KeySearchWiki is a dataset for evaluating keyword search systems over Wikidata.

The dataset was automatically generated by leveraging Wikidata and Wikipedia set categories (e.g., Category:American television directors) as data sources for both relevant entities and queries.
Relevant entities are gathered by carefully navigating the Wikipedia set categories hierarchy in all available languages. Furthermore, those categories are refined and combined to derive more complex queries.

Detailed information about KeySearchWiki and its generation can be found on the Github page.

This is a dump from Wikidata from 2018-12-17 in JSON. This one is not avavailable anymore from Wikidata. It was downloaded originally from https://dumps.wikimedia.org/other/wikidata/20181217.json.gz and recompressed to fit on Zenodo.

The dataset contains mappings between Wikidata entities and Wikipedia sections. The mappings come in addition to the existing Wikidata sitelinks referencing Wikipedia pages.

The creation of the present dataset stems from the observation that only a fraction of Wikidata entities has a corresponding Wikipedia article in any language (we refer to the remaining entities, without an article, as orphans). However, a substantial number of orphan entities are indeed available in Wikipedia, but not at the page level; orphan entities can be described within existing Wikipedia articles in the form of sections, subsections, and paragraphs of a more generic concept or fact. The dataset provides a fine-grained mapping between Wikidata orphan entities and Wikipedia (sub)-sections.

Mappings are provided for English language.

The dataset is available in JSON and RDF formats and complies with the Wikibase data model.

In the JSON representation, an entity contains two fields: id (the unique identifier of an entity) and sectionlinks (links to Wikipedia sections). Each sectionlink record comprises a list of records¹ with three fields: site, title, and url. A section title is appended to the page title separated with # symbol. Such a compound title is then URL-encoded and added to the URL path. Following the Wikidata guidelines, each entity is encoded as a single line.

Example:

{
  "id": "Q715509",
  "sectionlinks": {
    "enwiki": [
      {
        "site": "enwiki",
        "title": "Places in Harry Potter#Azkaban",
        "url": "https://en.wikipedia.org/wiki/Places_in_Harry_Potter#Azkaban"
      }
    ],
  }
}

The RDF dump is serialized using the Turtle format and stores nodes describing Wikipedia links. Section titles are added in the same manner as described above.

Example:

<https://en.wikipedia.org/wiki/Places_in_Harry_Potter#Azkaban> a schema:Article ;
    schema:about wd:Q715509 ;
    schema:inLanguage "en" ;
    schema:isPartOf <https://en.wikipedia.org/> ;
    schema:name "Places in Harry Potter#Azkaban"@en .

<https://en.wikipedia.org/> wikibase:wikiGroup "wikipedia" .

¹ As opposed to sitelinks, where each entity can be mapped with a unique Wikipedia page (one-to-one mapping), in sectionlinks we allow a one-to-many mapping, i.e., an entity can be mapped to multiple sections. For example, Tennis racket concept can be mapped to Tennis#Rackets and Racket (sports equipment)#Tennis sections.

Wikidata all data dump 時間: 2020/2/10 格式:JSON 檔案格式:gz

A BitTorrent file to download data with the title 'wikidata-20240902-all.json.bz2'

This dataset contains triples curated from Wikidata surrounding news events with causal relations, and is released as part of our WWW'23 paper, "Event Prediction using Case-Based Reasoning over Knowledge Graphs".

Starting from a set of classes that we consider to be types of "events", we queried Wikidata to collect entities that were an instanceOf an event class and that were connected to another such event entity by a causal triple (https://www.wikidata.org/wiki/Wikidata:List_of_properties/causality). For all such cause-effect event pairs, we then collected a 3-hop neighborhood of outgoing triples.

This is a collection of Gender Indicators from Wikidata and Wikipedia of Human Biographies. Data is derived from the 2016-01-03 Wikidata snapshot.Each file describe the humans in Wikidata aggregated by Gender (Property:P21), and dissaggregated by the following Wikidata Properties: - Date of Birth (P569)- Date of Death (P570)- Place of Birth (P19)- Country of Citizenship (P27)- Ethnic Group (P172)- Field of Work (P101)- Occupation (P106)- Wikipedia Language ("Sitelinks") Further aggregations of the data are: - World Map (Countries derived from place of birth and citizenship)- World Cultures (Inglehart Welzel Map applied to World Map)- Gender Co-Occurence (Humans with multiple genders).Wikidata labels have be translated to English for convenience when possible. You may still see values with "QIDs" which means there was no English translation possible. In the case where there were multiple values, such as for occupation, the we count the gender as co-occuring with each occupation separately.For more information. http://wigi.wmflabs.org/

This dataset contains information about commercial organizations (companies) and their relations with other commercial organizations, persons, products, locations, groups and industries. The dataset has the form of a graph. It has been produced by the SmartDataLake project (https://smartdatalake.eu), using data collected from Wikidata (https://www.wikidata.org).

This dataset contains quality labels for 5000 Wikidata items applied by Wikidata editors. The labels correspond to the quality scale described at https://www.wikidata.org/wiki/Wikidata:Item_quality Each line is a JSON blob with the following fields: - item_quality: The labeled quality class (A-E)- rev_id: the revision identifier of the version of the item that was labeled- strata: The size of the item in bytes at the time it was sampled- page_len: The actual size of the item in bytes- page_title: The Qid of the item- claims: A dictionary including P31 "instance-of" values for filtering out certain types of itemsThe # of observations by class is: - A class: 322- B class: 438- C class: 1773- D class: 997- E class: 1470

Profiles of politically exposed persons from Wikidata, the structured data version of Wikipedia.

Dataset SummaryThe Triple-to-Text Alignment dataset aligns Knowledge Graph (KG) triples from Wikidata with diverse, real-world textual sources extracted from the web. Unlike previous datasets that rely primarily on Wikipedia text, this dataset provides a broader range of writing styles, tones, and structures by leveraging Wikidata references from various sources such as news articles, government reports, and scientific literature. Large language models (LLMs) were used to extract and validate text spans corresponding to KG triples, ensuring high-quality alignments. The dataset can be used for training and evaluating relation extraction (RE) and knowledge graph construction systems.Data FieldsEach row in the dataset consists of the following fields:subject (str): The subject entity of the knowledge graph triple.rel (str): The relation that connects the subject and object.object (str): The object entity of the knowledge graph triple.text (str): A natural language sentence that entails the given triple.validation (str): LLM-based validation results, including:Fluent Sentence(s): TRUE/FALSESubject mentioned in Text: TRUE/FALSERelation mentioned in Text: TRUE/FALSEObject mentioned in Text: TRUE/FALSEFact Entailed By Text: TRUE/FALSEFinal Answer: TRUE/FALSEreference_url (str): URL of the web source from which the text was extracted.subj_qid (str): Wikidata QID for the subject entity.rel_id (str): Wikidata Property ID for the relation.obj_qid (str): Wikidata QID for the object entity.Dataset CreationThe dataset was created through the following process:1. Triple-Reference Sampling and ExtractionAll relations from Wikidata were extracted using SPARQL queries.A sample of KG triples with associated reference URLs was collected for each relation.2. Domain Analysis and Web ScrapingURLs were grouped by domain, and sampled pages were analyzed to determine their primary language.English-language web pages were scraped and processed to extract plaintext content.3. LLM-Based Text Span Selection and ValidationLLMs were used to identify text spans from web content that correspond to KG triples.A Chain-of-Thought (CoT) prompting method was applied to validate whether the extracted text entailed the triple.The validation process included checking for fluency, subject mention, relation mention, object mention, and final entailment.4. Final Dataset Statistics12.5K Wikidata relations were analyzed, leading to 3.3M triple-reference pairs.After filtering for English content, 458K triple-web content pairs were processed with LLMs.80.5K validated triple-text alignments were included in the final dataset.

RDF dump of wikidata produced with wdumps.

    <p>
    <br>
    <a href="https://tools.wmflabs.org/wdumps/dump/2049">View on wdumper</a>
    </p>

    <p>
    <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
    </p>

Wikidata offers a wide range of general data about our universe as well as links to other databases. The data is published under the CC0 "Public domain dedication" license. It can be edited by anyone and is maintained by Wikidata's editor community.