Suppose you want to buy a "Star Wars Trilogy" boxed set online, and you have some basic criteria for your purchase. First, you want widescreen, not full-screen, DVDs, and you want the set that has the extra disc of bonus materials. Second, you want the lowest available price, but you'd prefer to buy a new set, not a used one. Finally, you don't want to pay too much for shipping and handling, but you also don't want to wait too long for delivery.
At this point in the evolution of the Web, your best bet would be to look at different retailers' web pages, comparing prices and shipping times and rates. You could also look for a site that will compare prices and shipping options from several retailers all at once. Either way, you have to do most of the virtual legwork, then make your buying decision and place your order yourself.
With the Semantic Web, you'd have another option. You could enter your preferences into a computerized agent, which would search the Web, find the best option for you, and place your order. The agent could then open personal finance software on your computer and record the amount you spent, and it could mark the date your DVDs should arrive on your calendar. Your agent would also learn your habits and preferences, so if you had a bad experience buying from one particular site it would know not to use that site again.
The agent would do this not by looking at pictures and reading descriptions like a person does, but by searching through metadata that clearly identify and define what the agent needs to know. Metadata are simply machine-readable data that describe other data. In the Semantic Web, metadata are invisible as people read the page, but they're clearly visible to computers. Metadata can also allow more complex, focused Web searches with more accurate results. To paraphrase Tim Berners-Lee, inventor of the World Wide Web, these tools will let the Web -- currently similar to a giant book -- become a giant database.
We'll look at the tools that can make documents machine readable next.