free html hit counter WebFountain Gets Serious | John Battelle's Search Blog

WebFountain Gets Serious

By - February 05, 2004

WebFountain, which I posted about here, and which started as Clever, Jon Kleinberg’s project, is getting more buzz, this time from the Merc.

It’s interesting to note IBM’s strategy here. They are not opening it up to the world and creating another Alta Vista/Google moment, though I believe they clearly could. They are keeping this as a behind the scenes, OEM/consulting play. Their bread is not buttered in consumerland. It’s all about the enterprise, and marketing support, or “buzz reports” as the Merc calls them.

Buzz…or buzzkill? Check this passage from the Merc’s rather short piece:


Gruhl says another client, a security company, wanted to be able to predict for banks whether customers depositing large amounts of cash are connected to money launderers. WebFountain gathered publicly available information, as well as a corporate client’s own internal files, about known money launderers. It then searched through Web data — from newspaper wedding announcements to high school reunion Web sites — to draw any association between bank customers and known criminals. If the links show that someone’s wife has a best friend who is a money launderer, then the bank may have reason to refuse the customer’s money.

In contrast to standard search engines that just match patterns, WebFountain takes a subject and analyzes it in 50 different ways, noting how often someone’s name is associated with someone else’s, all in an effort to get a more precise answer to a query.

I’m sure the intelligence community hasn’t been paying attention….

Longer piece on WebFountain via ZDNet here.


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One thought on “WebFountain Gets Serious

  1. > I’m sure the intelligence community hasn’t been paying attention…

    That specific example being right up the alley of FinCEN (the Financial Crimes Enforcement Network – http://www.fincen.gov/ – a part of the Dept. of the Treasury).

    What’s really disconcerting about the example, though, is the suggestion that we’ll find inference used to black list, e.g., “because you’re married to someone who knows a moneylaunder, you are refused service.” And why would a bank refuse someone’s money? (As opposed to lending… it’s more reasonable that the bank should at least look a bit harder at you, if it’s giving, rather than accepting, money.) Because it worries that it might be accused by the government of aiding and abetting a (theoretical) money launderer?

    It feels like we’re on our way down a slippery slope toward “redlining by thoughtcrime,” with information services assisting in the gross categorization…