wikipedia biography dataset

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the free knowledge base with 113,194,393  data  items that anyone can edit .

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Wikidata is a free and open knowledge base that can be read and edited by both humans and machines.

Wikidata acts as central storage for the structured data of its Wikimedia sister projects including Wikipedia, Wikivoyage, Wiktionary, Wikisource, and others.

Wikidata also provides support to many other sites and services beyond just Wikimedia projects! The content of Wikidata is available under a free license , exported using standard formats , and can be interlinked to other open data sets on the linked data web.

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• What is Wikidata? Read the Wikidata introduction .
• Explore Wikidata by looking at a featured showcase item for author Douglas Adams (Q42) .
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• 2024-07-10: Wikidata records its 2,200,000,000th edit .
• 2024-07-10: The Wikidata development team held the Q3 Wikidata+Wikibase office hour on July 10th at 16:00 UTC. They presented their work from the past quarter and discussed what's coming next for Q3. Find the session log here .
• 2024-05-07: Wikidata records its 2 31 th edit , the revision IDs not fitting into 32-bit signed integer anymore
• 2024-04-10: The development team at WMDE held the 2024 Q2 Wikidata+Wikibase office hour in the Wikidata Telegram group. You can read session log .
• 2024-04: Wikidata held the Leveling Up Days , an online event focused on learning more about how to contribute to Wikidata from the 5th to 7th and 12th to 14th of April.

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• Tropical Storm Jongdari (Q129393829)
• Tropical Storm Shanshan (Q129555713)
• Mike Lynch (Q6833839) (pictured)
• Delta State College of Physical Education, Mosogar (Q107478063)
• Rabea Rogge (Q128996418)
• It Ends With Us (Q118641054)
• Christopher J. Morvillo (Q129401605)

Innovative applications and contributions from the Wikidata community

Featured WikiProject: WikiProject Music

Wikiproject Music is home to editors that help add data about artists, music releases, tracks, awards, and performances! Additionally, importing from and linking Wikidata with the many music databases and streaming services is another focus of the project. Read about our data model on our project page and come chat with us on Telegram.

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Various places that have Wikimedia datasets, and tools for working with them.

Also, you can now store table and maps data using Commons Datasets, and use them from all wikis from Lua and Graphs.

Tools to extract data from Wikipedia:

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W iki G raphs: A W ikipedia Text - Knowledge Graph Paired Dataset

Luyu Wang , Yujia Li , Ozlem Aslan , Oriol Vinyals

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[WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset](https://aclanthology.org/2021.textgraphs-1.7) (Wang et al., TextGraphs 2021)

• WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset (Wang et al., TextGraphs 2021)
• Luyu Wang, Yujia Li, Ozlem Aslan, and Oriol Vinyals. 2021. WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset . In Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15) , pages 67–82, Mexico City, Mexico. Association for Computational Linguistics.

Wikidata5m is a million-scale knowledge graph dataset with aligned corpus. This dataset integrates the Wikidata knowledge graph and Wikipedia pages. Each entity in Wikidata5m is described by a corresponding Wikipedia page, which enables the evaluation of link prediction over unseen entities.

The dataset is distributed as a knowledge graph, a corpus, and aliases. We provide both transductive and inductive data splits used in the original paper .

• Knowledge graph: Transductive split , 160 MB. Inductive split , 160 MB. Raw , 168 MB.
• Corpus , 991 MB.
• Entity & relation aliases , 188 MB.

For raw knowledge graph, it may also contain entities that do not have corresponding Wikipedia pages.

Wikidata5m follows the identifier system used in Wikidata. Each entity and relation is identified by a unique ID. Entities are prefixed by Q , while relations are prefixed by P .

Knowledge Graph

The knowledge graph is stored in the triplet list format. For example, the following line corresponds to <Donald Trump, position held, President of the United States> .

Each line in the corpus is a document, indexed by entity ID. The following line shows the description for Donald Trump .

Each line lists the alias for an entity or relation. The following line shows the aliases of Donald Trump .

Publications

• KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu, Zhengyan Zhag, Zhiyuan Liu, Juanzi Li, Jian Tang TACL 2021 arXiv BibTeX

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WIT (Wikipedia-based Image Text) Dataset is a large multimodal multilingual dataset comprising 37M+ image-text sets with 11M+ unique images across 100+ languages.

google-research-datasets/wit

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Wit : wikipedia-based image text dataset.

Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its size enables WIT to be used as a pretraining dataset for multimodal machine learning models.

Key Advantages

A few unique advantages of WIT:

• The largest multimodal dataset (publicly available at the time of this writing) by the number of image-text examples.
• A massively multilingual dataset (first of its kind) with coverage for 108 languages.
• First image-text dataset with page level metadata and contextual information
• A collection of diverse set of concepts and real world entities.
• Brings forth challenging real-world test sets.

You can learn more about WIT Dataset from our arXiv paper .

Latest Updates

2021 April: Happy to share the good news that our paper got accepted at SIGIR Conference . From ACM site, you can find our paper, slides and presentation .

2021 September: WIT Image-Text Competition is live on Kaggle. Our collaborators from Wikimedia Research blogged about this and they have made available the raw pixels and resnet50 embeddings for the images in this set. Here is our Google AI blog post .

2022 April: We are happy to share that the WIT paper and dataset was awarded the WikiMedia Foundation's Research Award of the Year ( tweet 1 , tweet 2 ). We are deeply honored and thank you for the recognition.

2022 May: We have released the WIT validation set and test set. Please see the data page for download links.

2022 Oct: Authoring Tools for Multimedia Content proposal accepted at TREC 2023

2023 Apr: AToMiC accepted at SIGIR 2023.

2023 Apr: WikiWeb2M Dataset released.

2023 May: Accepted submissions at WikiWorkshop 2023 .

• WikiWeb2M: A Page-Level Multimodal Wikipedia Dataset ( pdf , arXiv )
• Building Authoring Tools for Multimedia Content with Human-in-the-loop Relevance Annotations ( pdf )
• Characterizing Image Accessibility on Wikipedia across Languages ( pdf )

WIT Example

Wikipedia page.

For example, let's take the Wikipedia page for Half Dome, Yosemite in CA .

From the Wikipedia page for Half Dome : Photo by DAVID ILIFF. License: CC BY-SA 3.0

Wikipedia Page with Annotations of what we can extract

From this page, we highlight the various key pieces of data that we can extract - images, their respective text snippets and some contextual metadata.

By extracting and filtering these carefully, we get a clean, high quality image-text example that can be used in multimodal modeling.

Multimodal visio-linguistic models rely on a rich dataset to help them learn to model the relationship between images and texts. Having large image-text datasets can significantly improve performance, as shown by recent works. Furthermore the lack of language coverage in existing datasets (which are mostly only in English) also impedes research in the multilingual multimodal space – we consider this a lost opportunity given the potential shown in leveraging images (as a language-agnostic medium) to help improve our multilingual textual understanding.

To address these challenges and advance research on multilingual, multimodal learning we created the Wikipedia-based Image Text (WIT) Dataset. WIT is created by extracting multiple different texts associated with an image (e.g., as shown in the above image) from Wikipedia articles and Wikimedia image links. This was accompanied by rigorous filtering to only retain high quality image-text sets.

The resulting dataset contains over 37.6 million image-text sets – making WIT the largest multimodal dataset (publicly available at the time of this writing) with unparalleled multilingual coverage – with 12K+ examples in each of 108 languages (53 languages have 100K+ image-text pairs).

WIT: Dataset Numbers

WIT: Image-Text Stats by Language

We believe that such a powerful diverse dataset will aid researchers in building better multimodal multilingual models and in identifying better learning and representation techniques leading to improvement of Machine Learning models in real-world tasks over visio-linguistic data.

WIT Dataset is now available for download. Please check the data page.

If you use the WIT dataset, you can cite our work as follows.

This data is available under the Creative Commons Attribution-ShareAlike 3.0 Unported license.

Projects using WIT

For information regarding MURAL (Multimodal, Multitask Retrieval Across Languages) paper accepted at EMNLP 2021.

For any questions, please contact [email protected] .

If WIT dataset is useful to you, please do write to us about it. Be it a blog post, a research project or a paper, we are delighted to learn about it.

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Wikiqa (wikipedia open-domain question answering).

The WikiQA corpus is a publicly available set of question and sentence pairs, collected and annotated for research on open-domain question answering. In order to reflect the true information need of general users, Bing query logs were used as the question source. Each question is linked to a Wikipedia page that potentially has the answer. Because the summary section of a Wikipedia page provides the basic and usually most important information about the topic, sentences in this section were used as the candidate answers. The corpus includes 3,047 questions and 29,258 sentences, where 1,473 sentences were labeled as answer sentences to their corresponding questions.

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