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Many researchers and entrepreneurs are working on Internet-based knowledge-organizing technologies that stretch traditional definitions of the Web. Lately, some have been calling the technologies “Web 3.0.” But really, they’re closer to “Web 2.1.”

Typically, the name Web 2.0 is used by computer programmers to refer to a combination of a) improved communication between people via social-networking technologies, b) improved communication between separate software applications–read “mashups”–via open Web standards for describing and accessing data, and c) improved Web interfaces that mimic the real-time responsiveness of desktop applications within a browser window.

To see how these ideas may evolve, and what may emerge after Web 2.0, one need only look to groups such as MIT’s Computer Science and Artificial Intelligence Laboratory, the World Wide Web Consortium, Amazon.com, and Google. All of these organizations are working for a smarter Web, and some of their prototype implementations are available on the Web for anyone to try. Many of these projects emphasize leveraging the human intelligence already embedded in the Web in the form of data, metadata, and links between data nodes. Others aim to recruit live humans and apply their intelligence to tasks computers can’t handle. But none are ready for prime time.

The first category of projects is related to the Semantic Web, a vision for a smarter Web laid out in the late 1990s by World Wide Web creator Tim Berners-Lee. The vision calls for enriching every piece of data on the Web with metadata conveying its meaning. In theory, this added context would help Web-based software applications use the data more appropriately.

My current Web calendar, for example, knows very little about me, except that I have appointments today at 8:30 A.M. and 4:00 P.M. A Semantic Web calendar would not only know my name, but would also have a store of standardized metadata about me, such as “lives in: Las Vegas,” “born in: 1967,” “likes to eat: Thai food,” “belongs to: Stonewall Democrats,” and “favorite TV show: Battlestar Galactica.” It could then function much more like a human secretary. If I were trying to set up the next Stonewall Democrats meeting, it could sift through the calendars of other members and find a time when we’re all free. Or if I asked the calendar to find me a companionable lunch date, it could scan public metadata about the friends, and friends of friends, in my social network, looking for someone who lives nearby, is of a similar age, and appreciates Asian food and sci-fi.

Alas, there’s no such technology yet, partly because of the gargantuan effort that would be required to tag all the Web’s data with metadata, and partly because there’s no agreement on the right format for metadata itself. But several projects are moving in this direction, including FOAF, short for Friend of a Friend. FOAF files, first designed in 2000 by British software developers Libby Miller and Dan Brickley, are brief personal descriptions written in a standard computer language called the Resource Description Framework (RDF); they contain information such as a person’s name, nicknames, e-mail address, homepage URL, and photo links, as well as the names of the people that person knows.

I generated my own FOAF file this week using the simple forms at a free site called Foaf-a-matic and uploaded it to my blog site. In theory, other people using FOAF-enabled search software such as FOAF Explorer, or “identity hub” websites such as People Aggregator, will now be able to find me more easily.

Eventually, more may be possible. For example, I could instantly create a network of friends on a new social-networking service simply by importing my FOAF file. But for now, there aren’t a lot of ways to put your FOAF file to work.

Another project attempting to extract more meaning from the Web is Piggy Bank, a joint effort by MIT’s Computer Science and Artificial Intelligence Laboratory, MIT Libraries, and the World Wide Web Consortium. Piggy Bank’s goal is to lift chunks of important information in data-heavy websites from their surroundings, so that Web surfers can make use of these info chunks in new ways. For example, office address information extracted from LinkedIn, a professional networking site, could be fed into Google Maps, creating a map of my colleagues’ places of business.

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