|To:||edbark@xxxxxxxx, "[ontolog-forum] " <ontolog-forum@xxxxxxxxxxxxxxxx>|
|Cc:||"[ontolog-forum]" <ontolog-forum@xxxxxxxxxxxxxxxx>, ontolog-forum-bounces@xxxxxxxxxxxxxxxx|
|Date:||Thu, 3 Dec 2009 16:19:39 -0600|
My understanding is that instances in a domain identified by some symbol are asserted to be members of a specific Ontology concept, thus having potential property relationships to other individuals and their denotation symbols in other concepts in the Ontology. Whether these symbols denote real objects in a domain of discourse or states of affairs or situations is immaterial, the relationship between the symbol and something in the real or human conceptual world is entirely defined by humans.
If the labels used for concept and properties in the ontology are from some domain of discourse by people, the common understanding from shared background knowledge may be more accurately understood by a subset of people who know that domain adn they may have a better chance of understanding the expressions of the ontology in the language used. Yet! the ontology has very specific meaning asociated with its structure of relationships and rules of inference, which is a very small subset of knowledge in a domain. When I read an ontology i always have to concentrate on what the ontology is saying rather, and be very careful not to add meaning not defined by the ontology.
Is my understanding correct?
John A. Yanosy Jr.
I think we agree, but we are talking about two different topics.
>> It means exactly what it says and only what it says.
> Does not make sense without considering (at least a limited) social setting. The ontology means absolutely nothing if there is no human (or his/her software surrogate) to interpret it. "Meaning" is a human phenomenon - isn't it?
This is entirely correct. But, as I said, the intended audience for an
ontology -- a software artifact that represents knowledge in a formal
logic language -- is not primarily human. A human can read the ontology
and assign all kinds of loads to the terms, but the reason why a human
reads the ontology is only to determine whether it is suitable for use
by his software in performing his target application. A reasoning
engine, or another software tool, will only work with the formally
specified elements according to the formal meaning of the formal
language. The notion "meaning" for the software tooling is what
inferences it can make, what derived forms it can produce, what
decisions it will affect. And those will not be based on what the human
thinks the ontology terms "mean".
The human is judging whether the ontology is "practically consistent"
with his intent. And he may be wrong in his initial judgement. He may
get specific inferences he didn't expect. But that is exactly the same
learning process that two humans go through in establishing effective
communication. They think they have agreement on the concepts, terms
and rules, until some outlier case demonstrates that they don't have
quite the same concepts.
The idea here is that what the software will do with the ontology
concepts should be predictable (within the limits of competencies and
implementation errors), but what humans will do with any exchange is not
predictable, because we cannot really know exactly what meaning they
took from the exchange. What makes the human interactions work is
tolerance and fuzziness, but what makes the software work is the rigidity.
We do not yet know how to make software that reads natural language text
and behaves as predictably and as usefully as software that interprets
If your objective is to improve communication between humans, it is not
clear that an ontology is better than a text corpus. That said, the
strictures of a formal language force a discipline on the author of the
ontology that may improve the communication over what that person would
have written in natural language. And in fact, as some previous
discussion on this exploder revealed, a lot of knowledge engineers have
discovered that introducing formalism and graphical presentation of
concepts often significantly improves the ability of a group of domain
experts to achieve a reliable common terminology and understanding. It
gets them past the sloppy text that passes for communication of concepts
in their communities and the related presumption that unspecified
properties and constraints are part of the shared understanding.
You and I are probably in violent agreement.
My long-standing point is that knowledge engineering is an engineering
discipline; it is not a branch of philosophy or linguistics or cognitive
science, and it is not primarily about communications among humans. It
is about communicating between humans and software. The amazing thing
about AI software is that it doesn't have any idea what domain it
operates on, or what any of its manipulations actually "mean", but it
produces valuable results. But then again, the same is true of Newton's
calculus, and the IBM 704.
(I am reminded of a presentation I heard about 30 years ago from a
person from Shell Oil. He described an elaborate program for evaluating
their software performance in various ways and making improvements in
certain areas to reduce the total computational time. At the end, he
said: "Taken together, this set of improvements (which must have cost
several million dollars) has reduced our total computational load by
12%." And as the audience snickered, he followed that with: "Since the
Shell research center has 9 5M$ computer systems, that saving is one
entire computer system!" The meaning of a result is definitely
Edward J. Barkmeyer Email: edbark@xxxxxxxx
National Institute of Standards & Technology
Manufacturing Systems Integration Division
100 Bureau Drive, Stop 8263 Tel: +1 301-975-3528
Gaithersburg, MD 20899-8263 FAX: +1 301-975-4694
"The opinions expressed above do not reflect consensus of NIST,
and have not been reviewed by any Government authority."
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