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When Google first announced the Knowlege Graph, it named Freebase as one of its primary sources of structured data. It was named as one several public sources of information, which also included Wikipedia and the CIA World Factbook.

Google announced that it will be closing Freebase, and moving toward the Wikimedia Foundations Wikidata, which is described as a free linked database that can be read and edited by both humans and machines. It acts as central storage for the structured data of Wikipedia, Wikivoyage, Wikisource, and others.

WIkidata has simply been improving better than Freebase, so Google has decided to support that instead, and will be working to transition Freebase data over to Wikidata appropriately.

When we publicly launched Freebase back in 2007, we thought of it as a Wikipedia for structured data, So it shouldnt be surprising that weve been closely watching the Wikimedia Foundations project Wikidata] since it launched about two years ago, Googles Freebase says in a Google+ update (via Search Engine Roundtable). We believe strongly in a robust community-driven effort to collect and curate structured knowledge about the world, but we now think we can serve that goal best by supporting Wikidata theyre growing fast, have an active community, and are better-suited to lead an open collaborative knowledge base.

We should note that while Freebase has been around since 2007, its only belonged to Google since the company acquired Metaweb in 2010.

Google is helping transfer Freebase to Wikidata, and in the middle of next year, it will wind down the Freebase service as a standalone project. Theyll also launch a new API for entity search powered by Googles Knowledge Graph.

Loading Freebase into Wikidata as-is wouldnt meet the Wikidata communitys guidelines for citation and sourcing of facts while a significant portion of the facts in Freebase came from Wikipedia itself, those facts were attributed to Wikipedia and not the actual original non-Wikipedia sources, Freebase explains. So well be launching a tool for Wikidata community members to match Freebase assertions to potential citations from either Google Search or our Knowledge Vault, so these individual facts can then be properly loaded to Wikidata.

You may remember hearing about Knowledge Vault earlier this year.

Its a system of Googles, which stores information so that machines and people can read it. Its basically Googles giant database of facts. When you ask Google questions and get those direct answers, theyre likely coming from there. Rather than relying on crowdsourcing info like the Knowledge Graph, it uses an algorithm to pull info from the web, and turn it into raw data. You can get more in depth into it here.

We believe this is the best first step we can take toward becoming a constructive participant in the Wikidata community, but well look to continually evolve our role to support the goal of a comprehensive open database of common knowledge that anyone can use, Freebase says.

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Google Is Shutting Down Knowledge Graph Source Freebase

BalCCon2k14 - Hrvoje Bogner - Volunteered geographic information and OpenStreetMap
Its about Wikipedia and the involvement of women in wikipedia how they and everybody else can contribute? How to write good article and why we should contribute in wikipedia? Copyrights licenses...

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BalCCon2k14 - Hrvoje Bogner - Volunteered geographic information and OpenStreetMap - Video

roque santa cruz manchester city wikipedia

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roque santa cruz manchester city wikipedia - Video

PUBLIC RELEASE DATE:

16-Dec-2014

Contact: Abby Abazorius abbya@mit.edu 617-253-2709 Massachusetts Institute of Technology @MIT

By analyzing data on multilingual Twitter users and Wikipedia editors and on 30 years' worth of book translations in 150 countries, researchers at MIT, Harvard University, Northeastern University, and Aix Marseille University have developed network maps that they say represent the strength of the cultural connections between speakers of different languages.

This week, in the Proceedings of the National Academy of Sciences, they show that a language's centrality in their network -- as defined by both the number and the strength of its connections -- better predicts the global fame of its speakers than either the population or the wealth of the countries in which it is spoken.

"The network of languages that are being translated is an aggregation of the social network of the planet," says Cesar Hidalgo, the Asahi Broadcasting Corporation Career Development Assistant Professor of Media Arts and Sciences and senior author on the paper. "Not everybody shares a language with everyone else, and therefore the global social network is structured through these circuitous paths in which people in some language groups are by definition way more central than others. That gives them a disproportionate power and responsibility. On the one hand, they have a much easier time disseminating the content that they produce. On the other hand, as information flows through people, it gets colored by the ideas and the biases that those people have."

Plotting polyglots

Hidalgo and his students Shahar Ronen -- first author on the new paper -- and Kevin Hu, together with Harvard's Steven Pinker, Bruno Goncalves of Aix Marseille University, and Alessandro Vespignani of Northeastern, included a given Twitter user in their data set if he or she had at least three sentence-long tweets in a language other than his or her primary language. That left them with 17 million of Twitter's roughly 280 million users. They had similar thresholds for Wikipedia users who had edited entries in more than one language, which gave them a data set of 2.2 million Wikipedia editors.

In both cases, the strength of the connection between any two languages was determined by the number of users who had demonstrated facility with both of them.

The translation data came from UNESCO's Index Translationum, which catalogues 2.2 million book translations, in more than 1,000 languages, published between 1979 and 2011. There, the strength of the connection between two languages was determined by the number of translations between them.

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How information moves between cultures

There are a 100 billion stars in the Milky Way galaxy alone, and the Milky Way is relatively small on the cosmic scale. Luckily, there aren't nearly as many Wikipedia articles: with only 4,668,117 entries published to the English Wikipedia as I write this, stars outnumber those Wikipedia entries 1,867 to 1. From that perspective, Wikigalaxya beautiful new visualization of Wikipedia that transforms Wikipedia into a virtual galaxy and maps every entry to a star in a distant nebulaisn't exactly a one-to-one mapping. But when your core idea is this cool, it doesn't need to be.

Click a "star" and the associated Wikipedia entry loads in a panel on the left side of the screen, while the panel on the right shows any other articles related to it. Related articles will be in close proximity to one another, depending on how many links there are between them, and if enough articles interlink, Wikigalaxy will even have specific subject areas clustered together into star systems that, in real terms, would be millions of light years across (for example, the works of Philip K. Dick, or vacation spots). And if you really want to explore Wikipedia in a new way, you can click a "Fly" button to zoom between the stars of the Wikigalaxy, like some sort of "Citation Needed" Silver Surfer.

Designed by Owen Cornec, a French computer science student, and currently in beta, Wikigalaxy only has 100,000 Wikipedia articles mapped as of writing. But Cornec promises that by the time Wikigalaxy goes gold, every Wikipedia article will be represented. Check Wikigalaxy out for yourself here: if you've ever fallen down a Wikipedia wormhole (and who hasn't?), this is a fantastic visualization of where you end up.

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Fall Through A Wormhole Into This Stunning Wikipedia Galaxy