Wikipedia is the largest and most read online free encyclopedia currently existing. As such, Wikipedia offers a large amount of data on all its own contents and interactions around them, as well as different types of open data sources. This makes Wikipedia a unique data source that can be analyzed with quantitative data science techniques. However, the enormous amount of data makes it difficult to have an overview, and sometimes many of the analytical possibilities that Wikipedia offers remain unknown. In order to reduce the complexity of identifying and collecting data on Wikipedia and expanding its analytical potential, after collecting different data from various sources and processing them, we have generated a dedicated Wikipedia Knowledge Graph aimed at facilitating the analysis, contextualization of the activity and relations of Wikipedia pages, in this case limited to its English edition. We share this Knowledge Graph dataset in an open way, aiming to be useful for a wide range of researchers, such as informetricians, sociologists or data scientists.

There are a total of 9 files, all of them in tsv format, and they have been built under a relational structure. The main one that acts as the core of the dataset is the page file, after it there are 4 files with different entities related to the Wikipedia pages (category, url, pub and page_property files) and 4 other files that act as "intermediate tables" making it possible to connect the pages both with the latter and between pages (page_category, page_url, page_pub and page_link files).

The document Dataset_summary includes a detailed description of the dataset.

Thanks to Nees Jan van Eck and the Centre for Science and Technology Studies (CWTS) for the valuable comments and suggestions.

Knowledge graph generated from definitions extracted from Wikipedia articles.

Wikipedia Knowledge Graph Dataset

This dataset contains 49897 Wikipedia articles processed with two different models (sherlock_think and polaris_alpha) to extract structured knowledge.

  Data Format

The knowledge graphs are stored in Wolfram Language format, containing structured entities, relations, properties, and timeline events extracted from Wikipedia articles.

  Usage

from datasets import load_dataset

dataset =… See the full description on the dataset page: https://huggingface.co/datasets/AutomatedScientist/wiki-kg-dataset.

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.

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.

WikiCSSH If you are using WikiCSSH please cite the following: > Han, Kanyao; Yang, Pingjing; Mishra, Shubhanshu; Diesner, Jana. 2020. “WikiCSSH: Extracting Computer Science Subject Headings from Wikipedia.” In Workshop on Scientific Knowledge Graphs (SKG 2020). https://skg.kmi.open.ac.uk/SKG2020/papers/HAN_et_al_SKG_2020.pdf > Han, Kanyao; Yang, Pingjing; Mishra, Shubhanshu; Diesner, Jana. 2020. "WikiCSSH - Computer Science Subject Headings from Wikipedia". University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-0424970_V1 Download the WikiCSSH files from: https://doi.org/10.13012/B2IDB-0424970_V1 More details about the WikiCSSH project can be found at: https://github.com/uiuc-ischool-scanr/WikiCSSH This folder contains the following files: WikiCSSH_categories.csv - Categories in WikiCSSH WikiCSSH_category_links.csv - Links between categories in WikiCSSH Wikicssh_core_categories.csv - Core categories as mentioned in the paper WikiCSSH_category_links_all.csv - Links between categories in WikiCSSH (includes a dummy category called

DBPedia Classes

DBpedia is a knowledge graph extracted from Wikipedia, providing structured data about real-world entities and their relationships. DBpedia Classes are the core building blocks of this knowledge graph, representing different categories or types of entities.

Key Concepts:

Entity: A real-world object, such as a person, place, thing, or concept. Class: A group of entities that share common properties or characteristics. Instance: A specific member of a class.

Examples of DBPedia Classes:

Person: Represents individuals, e.g., "Barack Obama," "Albert Einstein." Place: Represents locations, e.g., "Paris," "Mount Everest." Organization: Represents groups, institutions, or companies, e.g., "Google," "United Nations." Event: Represents occurrences, e.g., "World Cup," "French Revolution." Artwork: Represents creative works, e.g., "Mona Lisa," "Star Wars."

Hierarchy and Relationships:

DBpedia classes often have a hierarchical structure, where subclasses inherit properties from their parent classes. For example, the class "Person" might have subclasses like "Politician," "Scientist," and "Artist."

Relationships between classes are also important. For instance, a "Person" might have a "birthPlace" relationship with a "Place," or an "Artist" might have a "hasArtwork" relationship with an "Artwork."

Applications of DBPedia Classes:

Semantic Search: DBPedia classes can be used to enhance search results by understanding the context and meaning of queries.

Knowledge Graph Construction: DBPedia classes form the foundation of knowledge graphs, which can be used for various applications like question answering, recommendation systems, and data integration.

Data Analysis: DBPedia classes can be used to analyze and extract insights from large datasets.

This repository contains two public knowledge graph datasets used in our paper Improving the Utility of Knowledge Graph Embeddings with Calibration. Each dataset is described below.

Note that for our experiments we split each dataset randomly 5 times into 80/10/10 train/validation/test splits. We recommend that users of our data do the same to avoid (potentially) overfitting models to a single dataset split.

wikidata-authors

This dataset was extracted by querying the Wikidata API for facts about people categorized as "authors" or "writers" on Wikidata. Note that all head entities of triples are people (authors or writers), and all triples describe something about that person (e.g., their place of birth, their place of death, or their spouse). The knowledge graph has 23,887 entities, 13 relations, and 86,376 triples.

The files are as follows:

entities.tsv: A tab-separated file of all unique entities in the dataset. The fields are as follows:

eid: The unique Wikidata identifier of this entity. You can find the corresponding Wikidata page at https://www.wikidata.org/wiki/.

label: A human-readable label of this entity (extracted from Wikidata).

relations.tsv: A tab-separated file of all unique relations in the dataset. The fields are as follows:

rid: The unique Wikidata identifier of this relation. You can find the corresponding Wikidata page at https://www.wikidata.org/wiki/Property:.

label: A human-readable label of this relation (extracted from Wikidata).

triples.tsv: A tab-separated file of all triples in the dataset, in the form of , , .

fb15krr-linked

This dataset is an extended version of the FB15k+ dataset provided by [Xie et al IJCAI16]. It has been linked to Wikidata using Freebase MIDs (machine IDs) as keys; we discarded triples from the original dataset that contained entities that could not be linked to Wikidata. We also removed reverse relations following the procedure described by [Toutanova and Chen CVSC2015]. Finally, we removed existing triples labeled as False and added predicted triples labeled as True based on the crowdsourced annotations we obtained in our True or False Facts experiment (see our paper for details). The knowledge graph consists of 14,289 entities, 770 relations, and 272,385 triples.

The files are as follows:

entities.tsv: A tab-separated file of all unique entities in the dataset. The fields are as follows:

mid: The Freebase machine ID (MID) of this entity.

wiki: The corresponding unique Wikidata identifier of this entity. You can find the corresponding Wikidata page at https://www.wikidata.org/wiki/.

label: A human-readable label of this entity (extracted from Wikidata).

types: All hierarchical types of this entity, as provided by [Xie et al IJCAI16].

relations.tsv: A tab-separated file of all unique relations in the dataset. The fields are as follows:

label: The hierarchical Freebase label of this relation.

triples.tsv: A tab-separated file of all triples in the dataset, in the form of , , .

CaLiGraph is a large-scale semantic knowledge graph with a rich ontology which is compiled from the DBpedia ontology, and Wikipedia categories & list pages. For more information, visit http://caligraph.org

Information about uploaded files: (all files are b-zipped and in the n-triple format)

caligraph-metadata.nt.bz2 Metadata about the dataset which is described using void vocabulary.

caligraph-ontology.nt.bz2 Class definitions, property definitions, restrictions, and labels of the CaLiGraph ontology.

caligraph-ontology_dbpedia-mapping.nt.bz2 Mapping of classes and properties to the DBpedia ontology.

caligraph-ontology_provenance.nt.bz2 Provenance information about classes (i.e. which Wikipedia category or list page has been used to create this class).

caligraph-instances_types.nt.bz2 Definition of instances and (non-transitive) types.

caligraph-instances_transitive-types.nt.bz2 Transitive types for instances (can also be induced by a reasoner).

caligraph-instances_labels.nt.bz2 Labels for instances.

caligraph-instances_relations.nt.bz2 Relations between instances derived from the class restrictions of the ontology (can also be induced by a reasoner).

caligraph-instances_dbpedia-mapping.nt.bz2 Mapping of instances to respective DBpedia instances.

caligraph-instances_provenance.nt.bz2 Provenance information about instances (e.g. if the instance has been extracted from a Wikipedia list page).

dbpedia_caligraph-instances.nt.bz2 Additional instances of CaLiGraph that are not in DBpedia. ! This file is no part of CaLiGraph but should rather be used as an extension to DBpedia. The triples use the DBpedia namespace and can thus be used to directly extend DBpedia. !

dbpedia_caligraph-types.nt.bz2 Additional types of CaLiGraph that are not in DBpedia. ! This file is no part of CaLiGraph but should rather be used as an extension to DBpedia. The triples use the DBpedia namespace and can thus be used to directly extend DBpedia. !

dbpedia_caligraph-relations.nt.bz2 Additional relations of CaLiGraph that are not in DBpedia. ! This file is no part of CaLiGraph but should rather be used as an extension to DBpedia. The triples use the DBpedia namespace and can thus be used to directly extend DBpedia. !

Changelog

v3.1.1

Fixed an encoding issue in caligraph-ontology.nt.bz2

v3.1.0

Fixed several issues related to ontology consistency and structure

v3.0.0

Added functionality to group mentions of unknown entities into distinct entities

v2.1.0

Fixed error that lead to a class inheriting from a disjoint class

Introduced owl:ObjectProperty and owl:DataProperty instead of rdf:Property

Several cosmetic fixes

v2.0.2

Fixed incorrect formatting of some properties

v2.0.1

Better entity extraction and representation

Small cosmetic fixes

v2.0.0

Entity extraction from arbitrary tables and enumerations in Wikipedia pages

v1.4.0

BERT-based recognition of subject entities and improved language models from spaCy 3.0

v1.3.1

Fixed minor encoding errors and improved formatting

v1.3.0

CaLiGraph is now based on a recent version of Wikipedia and DBpedia from November 2020

v1.1.0

Improved the CaLiGraph type hierarchy

Many small bugfixes and improvements

v1.0.9

Additional alternative labels for CaLiGraph instances

v1.0.8

Small cosmetic changes to URIs to be closer to DBpedia URIs

v1.0.7

Mappings from CaLiGraph classes to DBpedia classes are now realised via rdfs:subClassOf instead of owl:equivalentClass

Entities are now URL-encoded to improve accessibility

v1.0.6

Fixed a bug in the ontology creation step that led to a substantially lower amount of sub-type relationships than actually exist. The new version provides a richer type hierarchy that also leads to an increased amount of types for resources.

v1.0.5

Fixed a bug that has declared CaLiGraph predicates as subclasses of owl:Predicate instead of being of the type owl:Predicate.

Dataset

This dataset was created by Ved Prakash

Contents

A collection of SQLite database files containing all the data retrieved either from Wikidata/Wikipedia endpoints via SPARQL/MediaWiki API in the context of KeySearchWiki dataset generation.

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

We manage unique archives, documentation and photographic material and the largest art historical library on Western art from the Late Middle Ages to the present, with the focus on Netherlandish art. Our collections cover not only paintings, drawings and sculptures, but also monumental art, modern media and design. The collections are present in both digital and analogue form (the latter in our study rooms).

This knowledge graph represents our collection as Linked Data, primarily using the CIDOC-CRM and LinkedArt vocabularies.

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.

Kensho Derived Wikimedia Dataset

Wikipedia, the free encyclopedia, and Wikidata, the free knowledge base, are crowd-sourced projects supported by the Wikimedia Foundation. Wikipedia is nearly 20 years old and recently added its six millionth article in English. Wikidata, its younger machine-readable sister project, was created in 2012 but has been growing rapidly and currently contains more than 75 million items.

These projects contribute to the Wikimedia Foundation's mission of empowering people to develop and disseminate educational content under a free license. They are also heavily utilized by computer science research groups, especially those interested in natural language processing (NLP). The Wikimedia Foundation periodically releases snapshots of the raw data backing these projects, but these are in a variety of formats and were not designed for use in NLP research. In the Kensho R&D group, we spend a lot of time downloading, parsing, and experimenting with this raw data. The Kensho Derived Wikimedia Dataset (KDWD) is a condensed subset of the raw Wikimedia data in a form that we find helpful for NLP work. The KDWD has a CC BY-SA 3.0 license, so feel free to use it in your work too.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4301984%2F972e4157b97efe8c2c5ea17c983b1504%2Fkdwd_header_logos_2.jpg?generation=1580510520532141&alt=media" alt="">

This particular release consists of two main components - a link annotated corpus of English Wikipedia pages and a compact sample of the Wikidata knowledge base. We version the KDWD using the raw Wikimedia snapshot dates. The version string for this dataset is kdwd_enwiki_20191201_wikidata_20191202 indicating that this KDWD was built from the English Wikipedia snapshot from 2019 December 1 and the Wikidata snapshot from 2019 December 2. Below we describe these components in more detail.

Example Notebooks

Dive right in by checking out some of our example notebooks:

• Introduction to the KDWD Wikipedia sample
• Introduction to the KDWD Wikidata sample
• Entity aliases and disambiguation candidates from anchor link statistics

Updates / Changelog

• initial release 2020-01-31

File Summary

• Wikipedia
  • page.csv (page metadata and Wikipedia-to-Wikidata mapping)
  • link_annotated_text.jsonl (plaintext of Wikipedia pages with link offsets)
• Wikidata
  • item.csv (item labels and descriptions in English)
  • item_aliases.csv (item aliases in English)
  • property.csv (property labels and descriptions in English)
  • property_aliases.csv (property aliases in English)
  • statements.csv (truthy qpq statements)

Three Layers of Data

The KDWD is three connected layers of data. The base layer is a plain text English Wikipedia corpus, the middle layer annotates the corpus by indicating which text spans are links, and the top layer connects the link text spans to items in Wikidata. Below we'll describe these layers in more detail.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4301984%2F19663d43bade0e92f578255f6e0d9dcd%2Fkensho_wiki_triple_layer.svg?generation=1580347573004185&alt=media" alt="">

Wikipedia Sample

The first part of the KDWD is derived from Wikipedia. In order to create a corpus of mostly natural text, we restrict our English Wikipedia page sample to those that:

• are in the (Main/Article) namespace
• are not redirect pages
• are not disambiguation pages
• a...

Large knowledge graphs like DBpedia and YAGO are always based on the same source - namely Wikipedia. But there are more wikis that contain information about long-tail entities such as wiki hosting platforms like Fandom. In this paper, we present the approach and analysis of DBkWik++, a fused Knowledge Graph from thousands of wikis. A modified version of the DBpedia framework is applied to each wiki which results in many isolated Knowledge Graphs. With an incremental merge based approach, we reuse one-to-one matching systems to solve the multi source KG matching task. Based on this alignment we create a consolidated knowledge graph with more than 15 million instances.

Dataset

This dataset was created by Hiten

Contents

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.

This file contains the inductive split of Wikidata5m knowledge graph.

WikiEvents is a knowledge graph based dataset for NLP and event-related machine learning tasks.

This dataset includes RDF data in JSON-LD about events between January 2020 and December 2022. It was extracted from the Wikipedia Current events portal, Wikidata, OpenStreetMaps Nominatim and Falcon 2.0. The extractor is available on GitHub under semantic-systems/current-events-to-kg.

The RDF data for each month is split onto four graph modules each:

• The base graph module contains events, event summaries with references from named entities to Wikipedia articles.
• The ohg graph module with all one-hop graphs (ohg) around the referencend Wikidata entities.
• The osm graph module which contains spartial data from OpenStreetMap (OSM).
• The raw graph module containing the raw HTML objects of events and article infoboxes.

This repository additionally includes two JSON files with training samples used for entity linking and event-related location extraction. They were created using queries to the WikiEvents dataset uploaded into this repository.

PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)

Build Date: May 10, 2020

Please note that all resources linked below redirect to a publicly Google Cloud Storage bucket where all data are publicly accessible. Routing users from this wiki page is perfectly safe and allows us to avoid requiring users to have a Google account and login to download data. If you have any questions or concerns, please email the project maintainer at callahantiff@gmail.com.

If you have a Google account you can access the data directly via 👉 here

📚 For additional information on the builds please see the following README
🗂 For additional information on the KG file types please see the following Wiki page

🚨 AVAILABLE FILES 🚨Available KG benchmark files are zipped and listed below. For additional details on what each file contains, please see the associated Wiki page 👉 here.

QBLink-KG is a modified version of QBLink, which is a high-quality benchmark for evaluating conversational understanding of Wikipedia content.QBLink consists of sequences of up to three hand-crafted queries, with responses being single-named entities that match the titles of Wikipedia articles.For the QBLink-KG, the English subset of the DBpedia snapshot from September 2021 was used as the target Knowledge Graph. QBLink answers provided as the titles of Wikipedia infoboxes can be easily mapped to DBpedia entity URIs - if the corresponding entities are present in DBpedia - since DBpedia is constructed through the extraction of information from Wikipedia infoboxes.QBLink, in its original format, is not directly applicable for Conversational Entity Retrieval from a Knowledge Graph (CER-KG) because knowledge graphs contain considerably less information than Wikipedia. A named entity serving as an answer to a QBLink query may not be present as an entity in DBpedia. To modify QBLink for CER over DBpedia, we implemented two filtering steps: 1) we removed all queries for which the wiki_page field is empty, or the answer cannot be mapped to a DBpedia entity or does not match to a Wikipedia page. 2) For the evaluation of a model with specific techniques for entity linking and candidate selection, we excluded queries with answers that do not belong to the set of candidate entities derived using that model.The original QBLink dataset files before filtering are:QBLink-train.jsonQBLink-dev.jsonQBLink-test.jsonAnd the final QBLink-KG files after filtering are:QBLink-Filtered-train.jsonQBLink-Filtered-dev.jsonQBLink-Filtered-test.jsonWe used below references to construct QBLink-KG:Ahmed Elgohary, Chen Zhao, and Jordan Boyd-Graber. 2018. A dataset and baselines for sequential open-domain question answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1077–1083, Brussels, Belgium. Association for Computational Linguistics.https://databus.dbpedia.org/dbpedia/collections/dbpedia-snapshot-2021-09Lehmann, Jens et al. ‘DBpedia – A Large-scale, Multilingual Knowledge Base Extracted from Wikipedia’. 1 Jan. 2015 : 167 – 195.To give more details about QBLink-KG, please read our research paper:Zamiri, Mona, et al. "Benchmark and Neural Architecture for Conversational Entity Retrieval from a Knowledge Graph", The Web Conference 2024.