Jennifer Zaino
SemanticWeb.com Contributor

There’s a new Facebook application launching today that leverages the structured data within people’s profiles to help individuals test their knowledge about their friends and share their scores back with the community.

It’s a fun way of familiarizing the American audience with smart.fm, a web site developed by Cerego Japan designed to help consumers learn about subjects ranging from foreign languages to medicine to wine – or just about anything else it, content partners or consumers themselves create learning lists for. (If you need to know how to say the names of all the Pokemon characters in Japanese, you’ll find a list for it here.)

Cerego transported its methodology to improve learning and memory around fact-based information – such as high-frequency words and phrases within a language – some years ago for the b-to-b environment. It then transported that methodology to the web for general consumers, along with open tools that let people remix content for any area they are interested in.

The site lets users find and learn content in a number of verticals and categories; its adaptive learning engine creates a memory profile for every item users encounter for the duration of their experience on the site, and helps to monitor progress and guide users so that they can work on their weaknesses and build on their strengths.

The semantic web comes into the picture by enabling people to input content into the system in a structured fashion as they learn through the tool’s algorithms. “We want to tap into the existing resources of structured data and to redefine the use of that data for purposes such as learning,” says Andrew Smith Lewis, chairman and founder.

The Facebook application is the first iteration of a smart.fm application that pulls in structured data into a list mode to create content. But smart.fm, which will launch a redesigned user interface for its site in the fall, as well as an iPhone applications, also is experimenting with linked data resources such as Freebase. Still under construction but accessible is an application that uses Freebase’s structured data to build learning experiences.

Things can really get interesting – not just for users but for linked data sources such as Freebase – as users enhance that structured content, with anything from video to location tags, for all-new purposes. As the community builds on the content as part of enriching their learning experiences,

“we will freely share that back with Freebase, who will then have a set of data that’s really utilized and massaged and activated to use for completely other purposes,” says Smith Lewis. “The owners of these structured data sets are excited about what they get back.”

As the wealth of structured, semantic, and linked data rises, “we see a pretty interesting opportunity for some disruptions in the educational space,” he says. E-learning, he believes, has mostly mimicked the classroom experience rather than tried to take it to the last mile of utilizing technology to helping people really retain information.

“With the push to structured and linked data, that provides the ammunition and takes the power away from the content holders, who until now had all the cards,” he says. “Now there’s a phenomenal wealth of information unlocked, freely available and consumable – that’s a match made in heaven as far as we’re concerned.”

Learning applications can be created through smart.fm’s APIs and reside on any web site that wants to provide an environment for structured learning. Its open API is freely available to those who aim to use it to create open and shareable content, while those who want to create gated, for-fee content will be able to access its API for a fee. The company also earns income from sponsorship for its study content – for example, a wine vendor may sponsor a learning experience on the subject.

Smart.fm racks up the user experience on a number of levels – you can visit the site to learn, for example, that yesterday users racked up more than 6,000 actual study hours, studied more than 600,000 items, completed nearly 60,000 of them, and created almost 3,000 more. The community also has helped grow the platform, seeded with 14,000 words and phrases, to more than 3 million, with the content going against its natural language processing engine for tagging and multi-language translation.

Smith-Lewis notes that the company measures actually success with users through data that lets them understand their memory strength and progress against the content, as well as through anecdotal stories they hear from users about their individual success.

“At this point we validate the memory level and increase in learning for content within the system,” he says. Beyond that, it’s up to the individual user to make strides. “Your ability to apply [what you learn] in the real world is your deal.”

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