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Re: [ontolog-forum] What is "understanding" - was: Building on common gr

To: "[ontolog-forum]" <ontolog-forum@xxxxxxxxxxxxxxxx>
From: "John F. Sowa" <sowa@xxxxxxxxxxx>
Date: Sun, 30 Mar 2008 23:26:25 -0400
Message-id: <47F059E1.2010000@xxxxxxxxxxx>
Pat C. and John B.,    (01)

I'll accept parts of what both of you are saying, but with
many qualifications.  Before getting to the qualifications,
I'll quote an example I've used before.    (02)

Following are four sentences that use the same verb in
a similar syntactic pattern, but with very different,
highly domain-dependent senses:    (03)

  1. Tom supported the tomato plant with a stick.
  2. Tom supported his daughter with $20,000 per year.
  3. Tom supported his father with a decisive argument.
  4. Tom supported his partner with a bid of 3 spades.    (04)

Each sentence has the same basic syntax:    (05)

    NP1(name Tom)  V(supported)  NP2  Prep(with)  NP3    (06)

If you used a dependency parser with thematic (or case)
labels, NP1 represents the agent of the verb 'support',
NP2 represents the theme, and NP3 represents the instrument.    (07)

If we look at a typical dictionary (say the Merriam-Webster
7th Collegiate), we find 6 primary word senses, each with
1 to 6 subsenses.  Without using a computer, I'd pick the
following senses for each of the four sentences:    (08)

  1. Sense 4a: "to hold up or serve as a foundation or prop for"    (09)

  2. Sense 3: "to pay the costs of : maintain"    (010)

  3. Sense 2a2: "to uphold or defend as valid or right"    (011)

  4. Sense 2b3: "to bid as in bridge to show support for"    (012)

Making those choices requires quite a bit of background knowledge,
and it's definitely nontrivial with current technology.  But the
next question is what to do with that choice.  It might be useful
in machine translation for picking the correct verb in some target
language.  Perhaps a statistical translator with enough data could
do so.  But could that be called "understanding"?    (013)

For sentence #1, a person who knew something about gardening could
form a clear image of the relationship between the stick and the
plant.  But somebody who had never seen a tomato plant and had no
idea whether tomatoes grow on trees like plums or on the ground
like pumpkins wouldn't really understand that sentence.  Such a
person might be able to guess at the relationships, but could you
still say that the person "understands" the sentence?    (014)

What would sentence #4 mean to somebody who didn't play bridge?
Or to somebody who had no knowledge of card games of any kind?    (015)

Suppose we had a word-expert analyzer with an enormous amount
of information about each verb.  Would that be the best way to
organize the knowledge base?  Would you put some knowledge about
bridge or tomatoes into some rules for each verb, noun, and
adjective that might refer to bridge or to tomatoes?  Or would
it be better to put all the knowledge about bridge in a module
that deals with bridge and all the knowledge about tomatoes in
a module that deals with tomatoes?    (016)

With either way of organizing the knowledge -- by words or
by subject matter -- how would you relate the lexical info
about each word to the ontology and to the background
knowledge about how to play bridge or work in the garden?    (017)

If you intend to use logic, how much logic would be needed
for those sentences?  What would a theorem prover do to aid
understanding?  Could proving some theorem about tomatoes be
considered understanding?  Would forming a mental image (or
a virtual reality image) be closer to understanding?  How
would such images be related to the lexicon and the ontology?    (018)

Is it likely that a bunch of people (similar to the Wikipedians)
would be willing and able to enter the kinds of knowledge in the
kinds of formats necessary for a system that understands?    (019)

The Cyc project has been paying professional knowledge engineers
to enter such knowledge into their system for the past 22 years.
They had two million axioms in 2004, but Cyc still can't read
a book in order to build up its knowledge base.  How much more
would be needed?  Or is there some better way?  What way?    (020)

John Sowa    (021)

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