The semantic web as a concept has been around for quite some time. It pre-dates, Web 2.0 in fact, even though people sometimes refer to it as Web 3.0 or some other term that denotes its place as the next-next evolution of “teh Intertubes”.
I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web – the content, links, and transactions between people and computers. A ‘Semantic Web’, which should make this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines. The ‘intelligent agents’ people have touted for ages will finally materialize.
Heady stuff for sure.
When taken at that level, making the web semantic seems like a job for eggheads in white coats, at least to me, i.e. it’s a scientific pursuit with massive chunks of really dense code.
Reading this post on O’Reilly Radar the other day about Twitter as a source for semantic information, makes it seem much closer. Turns out the socialization of the ‘tubes may provide the best metadata against which smart algorithms (ha, al-gore-ithm) can produce really good, erm semantic, results.
The post on Radar is worth a read, even if you don’t use Twitter. Basically, the core point is that analyzing a person’s tweets can tell you a lot more about that person, even more than a social network profile would. The author, Nick Bilton, gives himself and his tweets as an example:
A quick perusal of my Tweets shows that I live in Brooklyn, NY, I work for The New York Times, teach at NYU/ITP, I travel somewhere once a month for work, I love gardening, cappuccinos, my Vespa , U.I./Design and hardware hacking, I’m a political news junkie, I read Gizmodo & NYTimes.com and I was looking for a new car for a while, but now have a MINI and I’m also friends with these people. That’s a treasure trove of data about me, and it’s semantic on a granular level about only my interests.
If you use Twitter, scan your tweets to see how much information you share, and then remember that tweets are indexed by search engines, unless you secure them. This provides enormous potential for an automatic recommendation service; I sometimes use Twitter for questions or recommendations anyway, and having a bot that could reply to specific requests, analyze my tweets and their network effects, and return an answer would be sweet.
This ties in nicely with the personal algorithm I’ve talked about in the past. Think about how much better your ‘tubes could be if you could apply a rating or even TiVo-style thumb up/down to everything.
Facebook does some of this with the News Feed, allowing you to control how content you see from your friends and set how valuable you find each friend’s content. As people pump more content into Facebook, the News Feed becomes better at showing you what you want to see. Still, it’s a closed system, so there’s no portability or application outisde Facebook. You’re locked in, by design.
I’ve extolled the virtues of Google Reader’s Shared Items several times. I find that browsing the Shared Items of people I know applies a social filter to the ‘tubes, which leads me to interesting content. If Google Reader added a thumb up/down or rating to feed items and feeds and used the Google Search backend to find similar content, that would produce great results.
Of course, the downside to the social or semantic web is the improvement of advertising models. I suppose this could be a good thing, but most people don’t like to be pitched. Worse yet, identity theft becomes much when you have a lot of personal information to use.
I guess opting in would be an acceptance of these potentially bad side effects. Would it be worth it to you?
So, I expect a social web to develop more quickly as people flock to social networks and provide information about themselves. As the algorithms get more advanced and people (and businesses) provide more information, the semantic web will take shape.
Pretty cool (and potentially scary) stuff.
Find the comments and share your thoughts.