UPDATED Download Wikipedia Knowledge Graph

Since the development of the Semantic Web, knowledge graphs have often been associated with linked open data projects, focusing on the connections between concepts and entities.[2][3] They are also historically associated with and used by search engines such as Google, Bing, Yext and Yahoo; knowledge-engines and question-answering services such as WolframAlpha, Apple's Siri, and Amazon Alexa; and social networks such as LinkedIn and Facebook.

Recent developments in data science and machine learning, particularly in graph neural networks and representation learning, have broadened the scope of knowledge graphs beyond their traditional use in search engines and recommender systems. They are increasingly used in scientific research, with notable applications in fields such as genomics, proteomics, and systems biology.[4]

Download Wikipedia Knowledge Graph

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The term was coined as early as 1972 by the Austrian linguist Edgar W. Schneider, in a discussion of how to build modular instructional systems for courses.[5] In the late 1980s, the University of Groningen and University of Twente jointly began a project called Knowledge Graphs, focusing on the design of semantic networks with edges restricted to a limited set of relations, to facilitate algebras on the graph. In subsequent decades, the distinction between semantic networks and knowledge graphs was blurred.

In 2007, both DBpedia and Freebase were founded as graph-based knowledge repositories for general-purpose knowledge. DBpedia focused exclusively on data extracted from Wikipedia, while Freebase also included a range of public datasets. Neither described themselves as a 'knowledge graph' but developed and described related concepts.

In 2012, Google introduced their Knowledge Graph,[7] building on DBpedia and Freebase among other sources. They later incorporated RDFa, Microdata, JSON-LD content extracted from indexed web pages, including the CIA World Factbook, Wikidata, and Wikipedia.[7][8] Entity and relationship types associated with this knowledge graph have been further organized using terms from the schema.org[9] vocabulary. The Google Knowledge Graph became a successful complement to string-based search within Google, and its popularity online brought the term into more common use.[9]

In addition to the above examples, the term has been used to describe open knowledge projects such as YAGO and Wikidata; federations like the Linked Open Data cloud;[18] a range of commercial search tools, including Yahoo's semantic search assistant Spark, Google's Knowledge Graph, and Microsoft's Satori; and the LinkedIn and Facebook entity graphs.[2]

The popularization of knowledge graphs and their accompanying methods have led to the development of graph databases such as Neo4j[20] and GraphDB.[21] These graph databases allow users to easily store data as entities their interrelationships, and facilitate operations such as data reasoning, node embedding, and ontology development on knowledge bases.

A knowledge graph formally represents semantics by describing entities and their relationships.[22] Knowledge graphs may make use of ontologies as a schema layer. By doing this, they allow logical inference for retrieving implicit knowledge rather than only allowing queries requesting explicit knowledge.[23]

In order to allow the use of knowledge graphs in various machine learning tasks, several methods for deriving latent feature representations of entities and relations have been devised. These knowledge graph embeddings allow them to be connected to machine learning methods that require feature vectors like word embeddings. This can complement other estimates of conceptual similarity.[24][25][26]

Models for generating useful knowledge graph embeddings are commonly the domain of graph neural networks (GNNs).[27] GNNs are deep learning architectures that comprise edges and nodes, which correspond well to the entities and relationships of knowledge graphs. The topology and data structures afforded by GNNS provides a convenient domain for semi-supervised learning, wherein the network is trained to predict the value of a node embedding (provided a group of adjacent nodes and their edges) or edge (provided a pair of nodes). These tasks serve as fundamental abstractions for more complex tasks such as knowledge graph reasoning and alignment.[28]

As new knowledge graphs are produced across a variety of fields and contexts, the same entity will inevitably be represented in multiple graphs. However, because no single standard for the construction or representation of knowledge graph exists, resolving which entities from disparate graphs correspond to the same real world subject is a non-trivial task. This task is known as knowledge graph entity alignment, and is an active area of research.[29]

Strategies for entity alignment generally seek to identify similar substructures, semantic relationships, shared attributes, or combinations of all three between two distinct knowledge graphs. Entity alignment methods use these structural similarities between generally non-isomorphic graphs to predict which nodes corresponds to the same entity.[30]

As the amount of data stored in knowledge graphs grows, developing dependable methods for knowledge graph entity alignment becomes an increasingly crucial step in the integration and cohesion of knowledge graph data.

The Google Knowledge Graph is a knowledge base from which Google serves relevant information in an infobox beside its search results. This allows the user to see the answer in a glance, as an instant answer. The data is generated automatically from a variety of sources, covering places, people, businesses, and more.[1][2]

Google's Knowledge Vault was meant to deal with facts, automatically gathering and merging information from across the Internet into a knowledge base capable of answering direct questions, such as "Where was Madonna born?" In a 2014 report, the Vault was reported to have collected over 1.6 billion facts, 271 million of which were considered "confident facts" deemed to be more than 90% true. It was reported to be different from the Knowledge Graph in that it gathered information automatically instead of relying on crowd-sourced facts compiled by humans.[15]

According to The Register in 2014 the display of direct answers in knowledge panels alongside Google search results caused significant readership declines for Wikipedia, from which the panels obtained some of their information.[16] Also in 2014, The Daily Dot noted that "Wikipedia still has no real competitor as far as actual content is concerned. All that's up for grabs are traffic stats. And as a nonprofit, traffic numbers don't equate into revenue in the same way they do for a commercial media site". After the article's publication, a spokesperson for the Wikimedia Foundation, which operates Wikipedia, stated that it "welcomes" the knowledge panel functionality, that it was "looking into" the traffic drops, and that "We've also not noticed a significant drop in search engine referrals. We also have a continuing dialog with staff from Google working on the Knowledge Panel".[17]

The algorithm has been criticized for presenting biased or inaccurate information, usually because of sourcing information from websites with high search engine optimization. It had been noted in 2014 that while there was a Knowledge Graph for most major historical or pseudo-historical religious figures such as Moses, Muhammad and Gautama Buddha, there was none for Jesus, the central figure of Christianity.[19][20] On June 3, 2021, a knowledge box identified Kannada as the ugliest language in India, prompting outrage from the Kannada-language community; the state of Karnataka, where most Kannada speakers live, also threatened to sue Google for damaging the public image of the language. Google promptly changed the featured snippet for the search query and issued a formal apology.[21][22]

Rich category hierarchies like the one in Wikipedia are graphs and extremely useful for recommendation or graph-enhanced search. Have a look at the queries in QG#2 and the ones in the interactive guide for some ideas.

WikiGraphs is a dataset of Wikipedia articles each paired with a knowledge graph, to facilitate the research in conditional text generation, graph generation and graph representation learning. Existing graph-text paired datasets typically contain small graphs and short text (1 or few sentences), thus limiting the capabilities of the models that can be learned on the data.

WikiGraphs is collected by pairing each Wikipedia article from the established WikiText-103 benchmark with a subgraph from the Freebase knowledge graph. This makes it easy to benchmark against other state-of-the-art text generative models that are capable of generating long paragraphs of coherent text. Both the graphs and the text data are of significantly larger scale compared to prior graph-text paired datasets.

Explainable artificial intelligence (XAI) requires domain information to explain a system's decisions, for which structured forms of domain information like Knowledge Graphs (KGs) or ontologies are best suited. As such, readily available KGs are important to accelerate progress in XAI. To facilitate the advancement of XAI, we present the Wikipedia Knowledge Graph (WKG), based on information from English Wikipedia. Each Wikipedia article title, its corresponding category, and the category hierarchy are transformed into different entities in the knowledge graph. As the Wikipedia category hierarchy is not a tree, instead forming a graph, to make the finding process of the parent category easier, we break cycles in the category hierarchy. We evaluate whether the WKG is helpful to improve XAI compared with existing KGs, finding that WKG is better suited than the current state of the art. We also compare the cycle-free WKG with the Suggested Upper Merged Ontology (SUMO) and DBpedia schema KGs, finding minimal to no information loss. ff782bc1db