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In knowledge representation and reasoning, a knowledge graph is a knowledge base that uses a graph-structured data model or topology to represent and operate on data. Knowledge graphs are often used to store interlinked descriptions of entities – objects, events, situations or abstract concepts – while also encoding the free-form semantics ...
Wikidata is a collaboratively edited multilingual knowledge graph hosted by the Wikimedia Foundation. [2] It is a common source of open data that Wikimedia projects such as Wikipedia , [ 3 ] [ 4 ] and anyone else, is able to use under the CC0 public domain license.
A knowledge graph is a knowledge base that uses a graph-structured data model. ... Wikidata This page was last edited on 20 November 2023, at 11:08 (UTC). ...
Knowledge panel data about Thomas Jefferson displayed on Google Search, as of January 2015. 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 ...
On 16 December 2014, Google announced that it would shut down Freebase over the succeeding six months and help with the move of the data from Freebase to Wikidata. [1] On 16 December 2015, Google officially announced the Knowledge Graph API, which is meant to be a replacement to the Freebase API. Freebase.com was officially shut down on 2 May 2016.
A knowledge graph is a knowledge base that uses a graph-structured data model. Knowledge Graph may also refer to: Google Knowledge Graph, a knowledge graph that powers the Google search engine and other services; Bing Knowledge Graph or Satori, used by the Bing search engine; LinkedIn Knowledge Graph (LKG), a knowledge base for LinkedIn
In representation learning, knowledge graph embedding (KGE), also referred to as knowledge representation learning (KRL), or multi-relation learning, [1] is a machine learning task of learning a low-dimensional representation of a knowledge graph's entities and relations while preserving their semantic meaning.
The automatic generation of new descriptions reduces the human efforts in creating them and enriches Wikidata-based knowledge graphs. Our paper shows a practical impact on Wikipedia and Wikidata since there are thousands of missing descriptions." From the introduction: