Dear John, Richard, Azamat, Doug, et al,
I can understand if you don't find the patent
example comfortable, and want to try another area
as our common focus. But I also have trouble with
the VivoMind example because it is closed - you
have code and methods that have not been well
defined and described for public consumption. I
suggested patents because there is such a regular
form to them, and because the database is open to
public query and comparison.
We need a SMALL example, a SIMPLE example, and a
WELL DOCUMENTED world, IMHO, to make progress on
Doug's already well developed nucleus of
microtheory, which is a great starting point.
What can we choose that will be acceptable to all
of us, small enough to make progress with, and yet
able to clear up our early diversity of
viewpoints? I am open to suggestions, but so far
none of us have come up with the right focus yet.
At one point, you wanted to use biosemiotics and
apply Peirce's thoughts to the problem. So I
tried to respond with Use Case 1, but that hasn't
stirred up progressive discussions either. So I
tried Use Case 2 which was more abstract. We
could map Use Case 2 onto Use Case 1 and perhaps
deal with the self-interest versus interest issue.
Would that help us meet on a common ground for
clearing up the self-interest vs interest issues?
-Rich
Sincerely,
Rich Cooper
EnglishLogicKernel.com
Rich AT EnglishLogicKernel DOT com
9 4 9 \ 5 2 5 - 5 7 1 2
-----Original Message-----
From: ontolog-forum-bounces@xxxxxxxxxxxxxxxx
[mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx] On
Behalf Of John F. Sowa
Sent: Sunday, August 28, 2011 12:48 PM
To: [ontolog-forum]
Subject: [ontolog-forum] Semantics of Natural
Languages
Doug, Rich, and Azamat,
I changed the subject line because there are some
fundamental
differences in our assumptions and ways of
working. We're going
off in totally different directions that are
unlikely to converge.
Before going further, we should review where we
plan to go and how.
Doug worked at Cyc, and he understands the Cyc
ontology, knowledge
base, and methods of reasoning. He sat down and
wrote a precise,
well-defined microtheory based on the Cyc
ontology. It defines
a set of terms that we have been discussing in a
way that a system
such as Cyc could use to interpret sentences about
those terms,
draw inferences, and answer questions.
Rich started the thread for a self-interest
ontology because he
wanted to address questions about how governments
work: How are
the laws, policies, and regulations of a
government related to
the needs and interests of the people in a
community?
RC
> It seems to me that Doug’s initial ontology is
at the Theory level...
> Perhaps instead we should... try to make
progress elaborating Doug's
> formulation by experiment, observation or
classification,
> but in a more focused manner.
>
> I suggest... that we consider US patent
specifications as the narrow
> class of concise situation descriptions, problem
statements within
> that situation, and claimed embodiments of
solutions.
Azamat has a much grander goal of trying to
specify a global ontology
of everything. He wants something more
prescriptive than descriptive:
AA
> Nowadays, the scope of human and national
interest is formed by
> domineering politics, ideology, or commercial
concerns...
>
> Now a sensible ontology of self-interest is
supposed to raise the
> eco-awareness, eco-interest as well, to help
people see their
> responsibility for the environment...
I sympathize with all three of these goals. But
I'd like to describe
what we've been doing at our VivoMind company. We
have some good
tools, but we only have a very small group, and we
don't have any
research funding. We have to work on projects our
clients will pay
us to do, and we have to deliver the kinds of
results they want with
whatever budget they're willing to approve. But
at the same time,
we're trying to develop our technology in ways
that can solve very
challenging problems of natural language
understanding.
I agree with Rich "that Doug’s initial ontology
is at the theory level."
In fact, that has been one of the complaints about
Cyc that I discussed
with Lenat since the 1990s. But I strongly
disagree with the word
'narrow' in Rich's phrase: "the narrow class of
concise situation
descriptions, problem statements within that
situation, and claimed
embodiments of solutions."
I realize that Rich has done a lot of work with
patents, but there's
a huge difference between the narrow conventions
for stating the
patent application and the unbounded subject
matter of all the things
that could be patented. Furthermore, the patent
lawyers try to make
the invention sound novel by deliberately using
terminology that is
based on standard terms for the context, but
different in detail
from anything that anyone else has done.
It's conceivable that an ontology of self-interest
might relate
the inventor's self interest to the parts of a
patent application.
But that would also be "at the theory level", not
at the level
that any client is likely to pay somebody to do.
Before doing any work on patents, I want to see an
actual problem
that a real customer with real money is willing to
pay somebody
to solve. Give us some patents or patent
applications and a
specific problem that some paying client wants to
solve.
As for the environment, I would strongly support
any work that could
help preserve the environment, wildlife, etc. But
an ontology has
to clarify the subject matter. It should not have
any built-in
value judgments about what should or should not be
done.
Finally, I'd like to mention something about the
level of ontology
that we (at VivoMind) have found most useful. For
examples of
the applications we have worked on, see the
following slides:
http://www.jfsowa.com/talks/pursue.pdf
For an excerpt from the kind of "English", see
slide 27. Trying to
translate that text directly to CycL (or any other
kind of logic)
would be impossible. Instead, note the method
outlined in slide 26:
> Much easier task:
> ● Translate the COBOL programs to conceptual
graphs.
> ● Use the conceptual graphs from COBOL to
interpret the English.
> ● Use the analogy engine to compare the graphs
derived from COBOL
> to the graphs derived from English.
> ● Record the similarities and discrepancies.
The Intellitex parser used a general ontology with
very few axioms.
But the details needed for the application came
from analyzing
the data structures and definitions in COBOL,
adding all the
names of files and programs to the English
vocabulary, and
translating that information to conceptual graphs
(CGs).
Then those CGs served as the semantic foundation
for interpreting
the English sentences. Any sentences that did not
refer to anything
derived from the COBOL programs were ignored as
irrelevant.
Note that this application did not require any
predefined axioms
or any detailed ontology other than a simple
hierarchy of terms.
But it did require a very large and detailed
low-level ontology.
Fortunately, that ontology could be automatically
derived from
a formal language (COBOL).
I won't claim that this is the only way to do
language understanding,
but it worked very well. It shows how a very
detailed low-level
ontology can be used to solve a complex problem
without requiring
any axioms from an upper-level or mid-level
ontology. It did,
however, require the kind of lexical information
that could be
derived from WordNet or something similar.
I'll admit that more details at the upper and mid
levels would
often be useful. But it is also possible to
derive much of that
information automatically by analyzing documents
in English.
The application in slides 33 to 41 is an example
that shows how.
Slide 34 shows the source material, which included
79 documents,
some of which were research reports and others
were chapters
from a textbook on geology. Before answering any
queries,
the VivoMind system translated all the documents
to conceptual
graphs and indexed them with the Cognitive Memory
(TM) system.
Slide 40 shows how the query (a paragraph written
by a professional
geologist) was related to the answer by using
information from
Chapters 44 and 45 of the textbook.
The answer to the geologist's query was derived
from a research
report (McCaffrey and Kneller 2001), but it was
not possible
to match the sentences in the query directly to
that report.
However, Chapter 44 contained information about
three terms:
"lowstand fan", "passive margin", and "turbiditic
sandstones".
Chapter 45 contributed information about three
other terms:
"narrow feeder corridors", "stratigraphic onlap",
and
"intraslope basin".
Slide 41 summarizes the method:
> Emergent Knowledge
> When reading the 79 documents,
> ● VLP translates the sentences and paragraphs
to CGs.
> ● But it does not do any further analysis of
the documents.
> When a geologist asks a question,
> ● The VivoMind system may find related phrases
in many sources.
> ● To connect those phrases, it may need to do
further searches.
> ● The result is a conceptual graph that relates
the question to
> multiple passages in multiple sources.
> ● Some of those sources might contribute
information that does not
> have any words that came from the original
question.
> ● That new CG can be used to answer further
questions.
> By a “Socratic” dialog, the geologist can lead
the system to
> explore novel paths and discover unexpected
patterns.
Note that these applications show that the
detailed, low-level,
highly domain-dependent information is the most
important. But
that information is usually highly voluminous. It
is impractical
or impossible to define all of it in advance,
especially by hand.
But automated methods can often derive that kind
of information
from unstructured, natural language documents.
John
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