Post-Googleism At IBM With Piquant

kamesh writes "James Fallows of the New York Times reports an interesting search technology that IBM is developing. IBM demonstrated a system called Piquant, which analyzed the semantic structure of a passage and therefore exposed 'knowledge' that wasn't explicitly there. After scanning a news article about Canadian politics, the system responded correctly to the question, 'Who is Canada's prime minister?' even though those exact words didn't appear in the article. What do you think?"

Comments

Trust Issue

[Flamefly]

On a global scale this system tends to fall apart, there is a constant issue of trust when dealing with what looks to me, to be the holy grail of the semantic web.

What if 2 sites said the Prime Minister of Canada was Santa? explicity said it, would that overwrite the linked information? How would the system know what is right? You can't always just pick the majority answer, so you need to set up little areas of trust "I trust www.thisplace.com and everything it says" and that site in turn will say "I trust www.overhere.com" but who allocates the trust, couldn't those people be biased?

The semantic web will have a fantastic impact on the world, but the trust issue is something that needs to be addressed, and I don't see how it can ever, globally be done.

More likely we would have systems like this for individual sites, or intranets, trusted circles that would be unlikely to contradict themselves.

hopefully one day, if we truely get a global semantic web, we can see if the answer really is 42 :]

Re:Trust Issue

[ctr2sprt]

All search engines return a bunch of results ordered by those it thinks most likely address your search terms. One very simple way of ranking the results is popularity (number of pages with the same answer to your question). You could fine-tune the popularity index with a Google-ish reference counting algorithm.

One of the neatest approaches of this technology, I think, is the ability to eliminate search results. Anyone who's ever used Google to troubleshoot a problem knows that the first thirty or forty matches will all be the same: web mirrors of mailing lists or USENET posts. Using a vaguely semantic technology like this, Google could say, "Hey, all these pages are effectively identical" and collapse them into a single result.

This would be terribly useful for me, since I usually start my troubleshooting searches with an error message. Error messages in the Unix world being quite standardized, this nets me at least ten irrelevant "threads." Since each "thread" is duplicated about ten times in the Google results, that means the question I'm actually asking may not appear until page 5 or later. But using result grouping like this - which Google tries and is generally unsuccessful at - would mean I'd see my question asked on the first or second pages. Big improvement.

Another nifty trick would be an actual, working "related pages" link. So let's say I find my question, but, as is all too common, it's a question without an answer. I click on the link, the search engine does its magic, and it pulls up (perhaps) technical details on the software in question or alternate solutions to my problem. This is definitely going to be harder to implement than my other idea (perhaps even impossible for now), but it'd be really nice. It could make navigating the Internet like navigating Wikipedia or amazon.com.

Ah well. I can dream.

I wonder...

[Raul654]

Using google means that this would have to contend with a lot of noise - looking for one nugget of information on the internet will tend to yield a low signal-to-noise ratio. I wonder what would happen if instead, you were to run it using Wikipedia as a back end (full discosure - I'm a wikipedia admin). There'd be less information, but I suspect the quality of the results would be better.

Re:Latent Sematic Indexing

[timeOday]

I'm not sure if such a thing would out perform google for a general purpose search engine.

The short answer is no, because traditional information retrieval methods like LSI are easily fooled by spammer tricks like keyword stuffing.

The genius being google's success was paying *less* attention to the content of a page when categorizing it, and relying on links *to* the page instead. Why? Because of spammers.

Think about hiring for a job. You don't limit yourself to interviews with candidates, because the're highly motivated to decieve you. So you look for references. Certification is an example of this - somebody besides the person himself who will vouch for his competence. An even better reference is somebody you know and trust who thinks highly of the individual (which is why personal networking is so important to getting hired).

Google's PageRank is analogous. Instead of looking at the content of a page, you rely heavily on links to the page, especially links from more trusted sources. This helps defeat spammers, who use all manner of tricks to make their crap look good to search engine spiders.

Who is NOT Canada's prime minister?

[bob@dB.org]

I've worked for a company making a system that could easily answer a question like that. It really isn't hard to do. If you want to know how much of this is "black magic"/AI and how much is statistics, compare the results of the following two queries:

• Who is Canada's prime minister?
• Who is NOT Canada's prime minister?

If the system really understand the semantics of the indexed documents, the two result sets should be very different, and both should have a fair number of relevant documents.

If the system is just based on clever use of statistis, the two result sets will include a lot of the same documents, and the result set for the second query will probably have very few relevant documents.