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Semantic Web applications

Filed in: Web, WWW2004, Thu, May 20 2004 16:58 PT

From: New York City

How to make a Semantic Web browser

Dennis Quan of IBM Research presented Haystack, which was featured in Tim Berners-Lee’s keynote.

The baby step before automated agents is a user agent that focuses on personalizing information, to use the Semantic Web to deal with info overload. (Yes, please.) A Semantic Web user agent would bring related information together and put it into perspective for the user by creating relevant visualizations. The lessons from the user agents of today is that you have to provide decentralized access to information, and make it easy enough for Grandma to use it.

From this, Haystack. It allows users to go and suck down lots of data and rearrange it into views. He demonstrated bioinformatics, which is a rich area for data (in fact, W3C just got a fellow from a group called I3C which is working on all kinds of bio-science stuff). He pulled up one chunk of data, being ordered visually so people can try to make sense of it. Then he graphed it. As it goes on, it pulls in more data as it builds.

There are public databases of knowledge already out there, like the TAP project at Stanford, and IRC bots recording data. You can go and play with things like this, if you have a gig of free disk space and half a gig of RAM. So, not quite ready for prime-time, and not quite user-friendly enough yet, but at least there is a vision of how to sort data in the open world.

Semantic Email

Luke McDowell of the University of Washington presented a paper on semantic email based on a project conducted at UW.

The group set a baseline for what it would take in order to make semantic-based email transactions work broadly. It needs to be instantly gratifying to users. It needs to accommodate gradual adoption. And it needs to be easy to use. Semantic email processes provide at least the first of these.

Challenge #1: Process creation. They use templates, based in RDF, that are created by a simple Web form.

Challenge #2: Facilitating responses. They use text-based email forms that are then managed by a central server.

Challenge #3: Human/Machine Interoperability. Enabling either humans or software agents to handle semantic email messages. They have both human-readable and coded messages to allow either to respond.

This has been used for RSVPs, first-come-first-served situations, potlucks, consensus finding, and other areas like voting.

Why semantic email? Well, it may be able to determine certain things, such as that so-and-so probably can’t attend at all, based on her calendar. That is, to predict responses. It can interpret responses, and it can recommend points where intervention may be necessary. Say, I have a potluck, and there are too many desserts. This will need to be resolved.

Responses that violate the constraints of a situation have to be rejected. But then there are issues to resolve in determining whether constraints have been violated. The paper explains how to fix that.

What’s missing is that it doesn’t really define success, only various situations for failure, it ignores the costs of rejection (if the boss isn’t in, maybe it’s worth meeting anyway, or not), and assumes good actors.

They’ve tried to fix that by trying to specify a reward based on outcome, adding suggestions to those who have rejected, and come up with a probabilistic approach to get a positive outcome.

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