Sharing Knowledge via the World Wide Web

[jrineakter]

I don't have to describe the impact the Web has had on society, science, politics, and indeed every aspect of our lives. But for this story, the important thing to notice is that this new technology achieved just about every goal that we had set out for expert decision making systems; it became commonplace for physicians to come into an examination room with a computer tablet, and to look up symptoms, signs, conditions, diseases, and treatments.

Just as we had dreamed, our physician had access to all of medical knowledge at their fingertips.

Even such august institutions as the Mayo Clinic jumped on the bandwagon, producing websites to share the hard-won learnings of generations of Mayo practitioners with the web at large. And with this, something else happened, something that was beyond our wildest dreams when we were building expert systems; anyone with access to a computer could gain expertise in any area — just look up the information, and go to town. Not only had we improved the behavior of expert practitioners, but we had also made expertise available to the public.



It seemed to me that AI deserved its winter; so many of the things we wanted to achieve simply fell out of the transformation to the post-Web world. If what you really cared about was improving how people performed by providing them with knowledge, the Web was the way to go, not AI.

I wondered if there was anything to salvage from my years of studying AI and Knowledge Representation that would have any relevance in the new, post-Web world. At first, it seemed that the answer was "no." If you had something to sell, you could basically render your spreadsheet as web pages, sell online, and make a fortune. The expanded markets that could be reached by the web exploded in a cottage industry of miniature online storefronts. There was no need for knowledge or expert behavior or anything; if you built it, they would come… and buy.

But soon it became apparent that in order to buy something, you needed to find it, and the data that described products was important. That data was distributed — to bring it together, you needed to know what the data meant. And so was born the "Semantic Web" — the web that describes meaning.

I discovered to my delight that a lot of the methods for describing knowledge that we had developed back in "old AI" were relevant here. Many of these ideas have turned out to be very successful; graph representations of data (we used to call them "Semantic Nets"); descriptions of relationships between kinds of canada whatsapp number data things ("Concept hierarchies"); but most of all, the idea (pioneered in the '50s by LISP) that pointing to your data is more useful than copying it. I decided that I would study this new Semantic Web stuff, with an eye toward becoming an expert.

While the dreams we had back in the Expert Systems days had largely been fulfilled by the document web, I — along with Sir Tim Berners-Lee and others — felt that in the democratization of data, we could see humankind make advances beyond what we could imagine. I'm not talking about the data that would help someone sell better than just a spreadsheet on the web — though indeed, that drove a lot of advances; I mean data that would help the human condition. Data about molecular biology that help scientists discover cures for difficult diseases. Epidemiological data that help us track the effectiveness of treatments for a pandemic. Data about housing, crimes, demographics, and other social issues that would inform public policy. Data about the production, distribution, and consumption of food that allow us to address hunger on a worldwide scale.

I believe, quite sincerely, that data holds the key to the survival of the human race. And I believe that managing data on a global scale is key to making that data usable.

While saving the world is a laudable goal, it doesn't always pay the bills. The key to developing the technology to work at a global scale is to get it to work reliably on a smaller scale; this applies to any technology, not just data sharing technology. So I and several other consultants and companies realized that the same data-sharing dynamic is at work inside large enterprises.