Wikidata is a collaboratively edited knowledge base operated by the Wikimedia Foundation. It is intended to provide a common source of certain types of data which can be used by Wikimedia projects such as Wikipedia. Wikidata functions as a document-oriented database, centred on individual items. Items represent topics, for which basic information is stored that identifies each topic.

Wikidata is a free, CC0-licensed knowledge base operated by the Wikimedia Foundation. All public APIs (Wikibase REST, MediaWiki Action, SPARQL Query Service, Linked Data interface, Recent Changes event stream) are free of charge with no paid tiers. Heavy or commercial users are expected to follow the User-Agent and maxlag policies, mirror data via the database dumps, or contract with Wikimedia Enterprise for commercial-grade access.

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

With this feature the user is able to extend CSV datasets with existing information in the Wikidata KG. The tool applies entity linking to all concepts in the same column and enable the user to use the extracted entities to extend the dataset.

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

This dataset provides entity mappings between Freebase and Wikidata, enabling seamless integration between two large-scale knowledge graphs. It is based on the Wikidata data dump from October 28, 2013, and was originally published by Google under the CC0 (Public Domain) license.

The mappings are carefully filtered to ensure high reliability:

• Each mapping includes at least two shared Wikipedia links
• There are no conflicting Wikipedia links

This strict filtering results in high-confidence entity alignments, making the dataset useful for research and real-world applications in knowledge graph systems.

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

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

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.

FinDailyKG Wikidata Entity Enrichment KG is an incremental (daily/weekly) enrichment layer that converts news-derived entity “surfaces” into stable Wikidata QIDs and produces graph-ready Parquet tables for downstream analytics and GNN training.

What’s inside

Per-day node tables (examples): entity_nodes_day=*.parquet, org_nodes_day=*.parquet, industry_nodes_day=*.parquet

Per-day edge tables (examples): entity_mentions_day=*.parquet, org_industry_edges_day=*.parquet

Mapping tables: entity_qid_map_day=*.parquet (surface → QID), plus diagnostics/rejection tables for quality control

Caches (for fast incremental runs): surface_link_cache*.parquet, qid_meta_cache.parquet, class_parent_cache.parquet, wp_*_cache.parquet

Config + provenance artifacts: entitykg_config.json, run_metadata_day=*.json, MERGE_REPORT.json

How it’s produced (high level)

Start from news/entity surfaces (from FinDailyKG Dataset1, GDELT-based).

Link surfaces to Wikidata QIDs (with caching + retry/diagnostics).

Emit graph tables (nodes/edges) and diagnostics per day.

Designed to be re-run incrementally by reading the prior published version as a resume/cache seed.

Intended uses

Dynamic knowledge graph experiments (time-sliced KGs)

Entity linking / entity resolution analysis

Feature generation for financial NLP

Graph ML / GNN pipelines over daily snapshots

Wikidata QRank computed based on a modification of Wikidata QRank (source https://github.com/athalhammer/wikidata-qrank; originally developed by Sascha Brawer https://github.com/brawer/wikidata-qrank). It is computed based on sitelinks extracted via danker from Wikipedia projects (no wikibooks, no commons, etc).

Wikidata Title and Description

Wikidata entity titles and short descriptions for all 324 Wikipedia language editions, extracted from the Wikimedia Analytics wmf.wikidata_entity table. Each row links a Wikidata QID to the title and description as they appear in a specific language edition of Wikipedia.

  Dataset Details

Field Value

Source wmf.wikidata_entity (Wikidata + Wikipedia sitelinks)

Snapshot 2026-03-30

Languages 324

Total titles 88,292,409

Titles… See the full description on the dataset page: https://huggingface.co/datasets/wikimedia/wikidata-title-desc.

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

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.

RDF dump of wikidata produced with wdumps.

    <p>
    <br>
    <a href="https://tools.wmflabs.org/wdumps/dump/317">View on wdumper</a>
    </p>

    <p>
    <b>entity count</b>: 48701, <b>statement count</b>: 868911, <b>triple count</b>: 3044412
    </p>

A copy of a dump which was available from WikiMedia: https://dumps.wikimedia.org/wikidatawiki/entities/

Wikidata Thematic Subgraph Selection These datasets have been designed to train and evaluate algorithms to select thematic subgraphs of interest in a large knowledge graph from seed entities of interest. Specifically, we consider Wikidata. Given a set of seed QIDs of interest, a graph expansion is performed following P31, P279, and (-)P279 edges. Traversed classes that thematically deviates from seed QIDs of interest should be pruned. Datasets thus consist of classes reached from seed QIDs that are labeled as "to prune" or "to keep". Available datasets Dataset # Seed QIDs # Labeled decisions # Prune decisions Min prune depth Max prune depth # Keep decisions Min keep depth Max keep depth # Reached nodes up # Reached nodes down dataset1 455 5233 3464 1 4 1769 1 4 1507 2593609 dataset2 105 982 388 1 2 594 1 3 1159 1247385 Each dataset folder contains datasetX.csv: a CSV file containing one seed QID per line (not the complete URL, just the QID). This CSV file has no header. datasetX_labels.csv: a CSV file containing one seed QID per line and its label (not the complete URL, just the QID) datasetX_gold_decisions.csv: a CSV file with seed QIDs, reached QIDs, and the labeled decision (1: keep, 0: prune) datasetX_Y_folds.pkl: folds to train and test models based on the labeled decisions dataset1-2 consists of using dataset1 for training and dataset2 for testing. License Datasets are available under the CC BY-NC license.

RDF dump of wikidata produced with wdumps.

    <p>
    <br>
    <a href="https://tools.wmflabs.org/wdumps/dump/1752">View on wdumper</a>
    </p>

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

The free knowledge base anyone can edit https://wikidata.org

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