Neo4j GraphGist Design Docs On-line

UPDATE: I'm very pleased to note that this 2-part GraphGist won First Place in the Other category of the Neo4j GraphGist Winter Domain Modeling Challenge! :-)

I have been very quiet during the year-end holiday and throughout January. I have been busy in the 'deep weeds' of moving the FactMiners social-game ecosystem forward. Quick summary... The FactMiners game is the means we will use to create an incredible 'Fact Cloud' of all the information in all 48 issues of Softalk magazine. It is an ambitious mission, but one that is guaranteed to be "serious fun"– especially when we create a crowdsource game technology and community (that's the ecosystem scope of this).

FactMiners: Scientists Say It's a Great Idea!

Okay, the headline's unnamed scientists did not specifically say that the idea for the FactMiners social-game ecosystem we're developing in support of The Softalk Apple Project is a great idea. What they are saying is that game-powered crowdsourcing methods are a tremendous resource for doing real and important science research.

A Quick Trip to the Stanford Vision Lab

Fei-Fei Li is the director of the Computer and Human Vision Lab within the legendary Stanford Artificial Intelligence Laboratory. While her research interests and breakthrough contributions to the field are wide-ranging, I want to focus briefly on a 2009 study she and her colleagues did at Princeton, before Dr. Li's selection to head the prestigious Stanford Vision Lab.

Finding the 'CV' in 'STEM' at the British Library Image Collection

Computer Vision, or using its popular acronym 'CV', is a domain of scientific knowledge and practice within the domain of Artificial Intelligence which is part of the broad domain of Computer Science. CV is a particularly challenging field that has strong connections into each of the Science, Technology, Engineering, and Math 'branches' of the STEM fields of education.

Introducing the 'Seeing Eye Child' Robot Adoption Agency

The 'Seeing Eye Child' Robot Adoption Agency is similar to the 'Tamagotchi' or 'digital pet' gaming phenomena that hit in the mid-1990's and is still going strong. The difference here is that we harness our little cognitive learning machines – AKA FactMiners game players – to 'adopt' a robot (AKA a machine-learning program with some form of vision – image intake and analysis – capability) and help it to learn to see and understand its world. As an adoptive 'Seeing Eye Child', players take on the roles of coach and referee for training sessions where adopted robots learn to see what's in the British Library images.

A FactMiners' Fact Cloud for the British Library Image Collection

I was thrilled to read the announcement this week in the British Library Digital Scholarship blog about the Library's uploading to the Flickr Commons of over 1 million Public Domain images scanned from 17th, 18th, and 19th century books in the Library's physical collections. The Flickr image collection makes the individual images easily available for public use.

FactMiners: More or Less Folksonomy?

Our goal is to create a crowdsource-powered social gaming community that "comes to play" in museum and archive on-line repositories with the intensity of the multitudes who play Candy Crush or Words With Friends. And by playing FactMiners, this rabid game-playing community will produce two results.

In short, both the meansFactMiner exploratory-learning game-playing – and the endsthe "by-product" generation of the host collection's Fact Cloud – have value in their own right.

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