Intelligent Data And The Semantic Web

Last year I started writing about Web 3.0 -- the concept of the Semantic Web -- and over the last few weeks I've seen more and read more on this topic, which leads me to believe that it's gaining steam.

The Semantic Web refers to a layer of intelligence that could be applied over the Internet and tailored in a manner that would deliver a fully customized experience to users, providing them with what they would want, based on a set of algorithms that hypothesize what they like, want and need. I'm a huge fan of this concept because if you break it down, any given person probably only uses about 5% of what is available on the Internet. The Semantic Web would take this experience and flip it on its head, ensuring that what you want and need is exactly what you get, but anticipating when what you need might change into what you will want or need (with me so far?).

The apparent proof for this shift can be seen in the move towards data and such products as search, behavioral targeting and text-based advertising. Search is becoming more fluid and more customized to the content of the query, with results changing based on the detailed taxonomy of the request. In some cases I get maps while in other cases I get links to video or just standard, old-fashioned text results. Type in "Mickey Mouse," then type in "Mickey Mouse Club" and then "Mickey Mouse Club Disneyland," and see all the different results formats you get in return. The difference in the results page is based on a hypothetical engine that anticipates what you are looking for and tries to deliver it exactly.



The movement toward aggregating data and creating behavioral profiles for advertising, or even going one step farther and creating dynamic content based on real-time profiling, are examples of early-stage Semantic aspirations. The next step will be for an intelligence engine to be placed on major sites that anticipates what you are looking for and what you might need in the near future, depending on your age, life stage and other information far beyond what sites you've been on and what pages you might have viewed. The aggregation of data from the ISP level takes this one step closer, enabling dynamic content profiles to be created and attached to specific users, depending on their log-in info and their past experiences. Reading data on a macro level and understanding all of a consumer's personalized data on his or her start page as well as what she's purchased in Amazon could theoretically offer insights into what she needs and wants, and deliver that content customized only to her!

In-text advertising also leads me to see that the Semantic aspirations are coming. In-text advertising aspires to be a means of linking consumers with information they need based on where they are. It ties a hypothetical insight into what they need based on what they want and delivers in a simplistic format now, but with broader opportunities later. Imagine a future when intelligent text can link you to anywhere on the Web in just a second by being dynamically linked to an engine that hypothesizes your needs and wants based on that behavioral data I referenced previously. An entire page of content would be clickable, and a richer experience for all content would be quite enticing. Of course, that brings us back to my initial point: that too much data can create too large of a palette from which to work -- so in-text placements would need to become smarter and deliver that customized version of the Web directly to you.

Of course I could be overcomplicating things a bit, but I don't think so. These are all valid concepts given the movement towards data and the valuation of the companies in this space. Watch for the innovations in the next three years and tell me whether I'm right or wrong. Or tell me now if you have more insights into this topic by clicking through to the Spin Board and sharing your insights!

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