The Knowledge Graph is a knowledge base used by Google to enhance its search engine's search results with semantic-search information gathered from a wide variety of sources. Knowledge Graph display was added to Google's search engine in 2012, starting in the United States, having been announced on May 16, 2012. It provides structured and detailed information about the topic in addition to a list of links to other sites. The goal is that users would be able to use this information to resolve their query without having to navigate to other sites and assemble the information themselves. The short summary provided in the knowledge graph is often used as a spoken answer in Google Now searches.
According to some news websites, the implementation of Google's Knowledge Graph has played a role in the page view decline of various language versions of Wikipedia. As of the end of 2016, knowledge graph holds over 70 billion facts.
According to Google, the information in the Knowledge Graph is derived from many sources, including the CIA World Factbook, Wikidata, and Wikipedia. The feature is similar in intent to answer engines such as Wolfram Alpha and efforts such as Linked Data and DBpedia. As of 2012, its semantic network contained over 570 million objects and more than 18 billion facts about and relationships between different objects that are used to understand the meaning of the keywords entered for the search.
On December 4, 2012, the Knowledge Graph was introduced in seven more languages: Spanish, French, German, Portuguese, Japanese, Russian, and Italian. During the Google I/O conference in May 2013, Google's Amit Singhal presented on the future of search, explaining that a search engine's three primary functions will need to evolve and that search will need to: 1. Answer, 2. Converse, and 3. Anticipate. As part of his keynote talk Singhal asked: "A computer you can talk to? And it will answer everything you ask it?"
In August 2014, Google announced a new initiative, the Knowledge Vault, which derives much of its data from the Knowledge Graph and the sources thereof, as well as harvesting its own data, ranking its reliability and compiling all results into a database of over 1.6 billion facts collected by machine learning algorithms. On December 16, 2014, the Freebase and Knowledge Graph team at Google announced that Freebase would shut-down in late 2015 and that they would help to transfer all of its data over to Wikidata. In October 2016 Google announced that the Knowledge Graph now holds 70 billion facts.
Other companies' knowledge graphs:
- Microsoft Bing's Satori Knowledge Base, revealed to the public in mid-2013 (further details were not released)
- Yandex's Object Answer, released in 2015
- Yahoo! and Baidu also have such technologies.
- LinkedIn's Knowledge Graph, revealed to the public in Oct 2016. It is a large knowledge base built upon LinkedIn "entities" such as 450M members, 190M historical jobs, 9M companies, 35K skills, 24K titles, 28K schools, 1.5K fields of study, 600+ degrees, 500+ certificates, 200+ countries, among other entities. It is a dynamic graph updated in real time upon member profile changes and when new entities emerge.
According to Google, information in the knowledge graph powers a "knowledge panel", which is a box containing information, and the box is presented at the top of search results. In May 2016, The Washington Post reported that "knowledge panels and other sorts of 'rich answers' have mushroomed across Google, appearing atop the results on roughly one-third of its 100 billion monthly searches".
Dario Taraborelli of the Wikimedia Foundation says that Google's omission of sources in its knowledge panels is designed so that the knowledge panel will seem more authoritative. The Post reports that Google's knowledge panels are "frequently unattributed", such as a knowledge panel on the age of actress Betty White which is "as unsourced and absolute as if handed down by God". Google will frequently scrape information from websites with varying degrees of success, but the increased use of Schema markup means that information on websites can be more intelligently understood and utilized in the Knowledge Graph.
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