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Re: [ontolog-forum] More on Watson

To: "[ontolog-forum]" <ontolog-forum@xxxxxxxxxxxxxxxx>
From: "terry.longstreth" <terry.longstreth@xxxxxxxxxxx>
Date: Tue, 08 Mar 2011 12:18:17 -0500
Message-id: <4D7664D9.7070208@xxxxxxxxxxx>
It probably looks more contrived to me than it should because the Author's name is so wrong (should be Robert Louis Stevenson).
Terry Longstreth



John F. Sowa wrote:
This thread has been quiet for a while, but I came across a pointer
to a blog with answers to questions about Watson.  All the answers
were written by members of the Watson team:

http://blog.reddit.com/2011/02/ibm-watson-research-team-answers-your.html

They answered 14 questions on various issues.  At the end of this
note is question #3 and the answer, which shows the steps to process
a typical question.  Watson does a lot more than matching words.

The answer to question #6 included the following point:

"Watson was written in mostly Java but also significant chunks of
code are written in C++ and Prolog, all components are deployed and
integrated using UIMA."

Note that the excerpts below use a variant of Prolog notation.
Marking variables with a capital letter, such as X, is a typical
Prolog convention.  But the person who answered the question
probably removed the obligatory quotes around phrases like
"Stevenson's Treasure Island".

John
___________________________________________________________________

3. Can you walk us through the logic Watson would go through to answer
a question such as, "The antagonist of Stevenson's Treasure Island."
(Who is Long John Silver?)

Step One: Parses sentence to get some logical structure describing
the answer.  X is the answer.

antagonist(X).
antagonist_of(X, Stevenson's Treasure Island).
modifies_possesive(Stevenson, Treasure Island).
modifies(Treasure, Island)

Step Two: Generates Semantic Assumptions

island(Treasure Island)
location(Treasure Island)
resort(Treasure Island)
book(Treasure Island)
movie(Treasure Island)

person(Stevenson)
organization(Stevenson)
company(Stevenson)
author(Stevenson)
director(Stevenson)

person(antagonist)
person(X)

Step Three: Builds different semantic queries based on phrases, keywords 
and semantic assumptions.

Step Four: Generates 100s of answers based on passage, documents
and facts returned from 3. Hopefully Long-John Silver is one of them.

Step Five: For each answer formulates new searches to find evidence in 
support or refutation of answer -- score the evidence.

Positive Examples:
Long-John Silver the main character in Treasure Island.....
The antagonist in Treasure Island is Long-John Silver
Treasure Island, by Stevenson was a great book.
One of the great antagonists of all time was Long-John Silver
Richard Lewis Stevenson's book, Treasure Island features many great
characters, the greatest of which was Long-John Silver.

Step Six: Generate, get evidence and score new assumptions

Positive Examples: (negative examples would support other characters,
people, books, etc associated with any Stevenson, Treasure or Island)
Stevenson = Richard Lewis Stevenson
"by Stevenson" --> Stevenson's
main character --> antagonist

Step Seven: Combine all the evidence and their scores Based on analysis
of evidence for all possible answer compute a final confidence and link
back to the evidence.

Watson's correctness will depend on evidence collection, analysis and
scoring algorithms and the machine learning used to weight and combine 
the scores.

 
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