Conceptualist.com, By Sahar Sarid

Michael Reisman for MIT Technology writes:

Powerset, Inc., based in San Francisco, is on the verge of offering an innovative natural-language search engine, based on linguistic research at the Palo Alto Research Center (PARC). The engine does more than merely accept queries asked in the form of a question. The company claims that the engine finds the best answer by considering the meaning and context of the question and related Web pages.
“Powerset extracts deep concepts and relationships from the texts, and the users query and match them efficiently to deliver a better search,” Powerset CEO Barney Pell says.

Even though attempts have been made at natural-language search for decades, Powerset says that its system is different because it has solved some of the fundamental technological problems that have existed with this kind of search. It has done so by developing a product that is deep, computationally advanced, and still economically viable.

Pell says that it’s difficult to pinpoint one particular technological breakthrough, but he believes that Powerset’s superiority lies in the three decades of hard work by scientists at PARC. (PARC licensed much of its natural-language search technology to Powerset in February.) There was not one piece of technology that solved the problem, Pell says, but instead, it was the unification of many theories and fragments that pulled the project together.

“After 30 years, it’s finally reached a point where it can be brought into the world,” he says.

A key component of the search engine is a deep natural-language processing system that extracts the relationships between words; the system was developed from PARC’s Xerox Linguistic Environment (XLE) platform. The framework that this platform is based on, called Lexical Functional Grammar, enabled the team to write different grammar engines that help the search engine understand text. This includes a robust, broad-coverage grammar engine written by PARC. Pell also claims that the engine is better than others at dealing with ambiguity and determining the real meaning of a question or a sentence on a Web page. All these innovations make the system more adaptable, he says, so that it can extract deep relationships from text.

My opinion is they are barking the wrong tree. When writing about Hakia, I wrote more about the issue here.

Understanding the meaning in text/media isn’t solving the issue of understanding what a user intent is. Taking the above example, if I go to Hakia and type “tiger”?, which results should I get? animal? tank? art?

While there’s no doubt semantic search is important the ultimate relevancy is in personalization. There is no way you can serve the right results constantly without taking into consideration the individual user.

In reality this isn’t about the amount of time of a research but about the perspective of those who conduct the research. A good system is built on fundamentals, not on some mathematical equations nor the time of research.

I’m looking forward to the day they go live, to see what Powerset is all about.