Diffbot Is Using Computer Vision to Reinvent the Semantic Web
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started running it out of a dorm at Stanford. And people started adding a bunch of different kinds of URLs to Diffbot outside of classes, like they might add Craigslist if they were searching for a job or a product, or Facebook if they wanted to see if their ex’s profile had changed.
X: So I assume the name “Diffbot” related to comparing the old and new versions of a website and detecting the differences?
MT: Yes, but just doing deltas on Web pages doesn’t work too well. It turns out that on the modern Web, every page refresh changes the ads and the counters. You have to be a little more intelligent.
That’s where understanding the page comes into play. I was studying machine learning at Stanford, and in particular one project I had worked on was the vision system for the self-driving car [Stanford’s entry in the 2007 DARPA Urban Challenge]. This was the stereo camera system that would compute the depth of a scene and say, ‘This is a cactus, this is drivable dirt, this is not drivable dirt, this is a cliff, this is a very narrow passageway.’ I realized that one way of making Diffbot generalizable was to apply computer vision to Web pages. Not to say, ‘This is a cactus and this is a pedestrian,’ but to say, ‘This is an advertisement and this is a footer and this is a product.’
A human being can look at Web page and very easily tell what type of page it is without even looking at the text, and that is what we are teaching Diffbot to do. The goal is to build a machine-readable version of the entire Web.
X: Isn’t that what Tim Berners-Lee has been talking about for years—building a Semantic Web that’s machine-readable?
MT: It seems that every three years or so a new Semantic Web technology gets hyped up again. There was RSS, RDF, OWL, and now it’s Open Graph and the Knowledge Graph. The central problem—why none of these have really gone mainstream—is that you are requiring humans to tag the content twice, once for the machine’s benefit and once for the actual humans. Because you are placing so much onus on the content creators, you are never going to have all of the content in any given system. So it will be fragmented into different Semantic Web file formats, and because of that you will never have an app that allows you to search and evaluate all that information.
But what if you analyze the page itself? That is where we have an opportunity, by applying computer vision to eliminate the problem of manual tagging. And we have reached a certain point in the technology continuum where it is actually possible—where the CPUs are fast enough and the machine learning technology is good enough that we have a good shot of doing it with high accuracy.
X: Why are you so convinced that a human-tagged Semantic Web would never work?
MT: The number one point is that people are lazy. The second is that people lie. Google used to read the meta tags and keywords at the top of a Web page, and so people would start stuffing those areas with everything. It didn’t correspond to what actual humans saw. The same thing holds for Semantic Web formats. Whenever you have things indexed separately, you start to see spam. By using a robot to look at the page, you are keeping it above that.
X: Talk about the computer vision aspect of Diffbot. How literal is the comparison to the cameras and radar on robot cars?