To: | "ONTAC-WG General Discussion" <ontac-forum@xxxxxxxxxxxxxx> |
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From: | "Cassidy, Patrick J." <pcassidy@xxxxxxxxx> |
Date: | Fri, 7 Oct 2005 08:15:59 -0400 |
Message-id: | <6ACD6742E291AF459206FFF2897764BE51D46E@xxxxxxxxxxxxxxxxx> |
Gary,
Concerning your question:
>>> The thrust of my
issue is that while a completed ontology might avoid the issues of language and
knowledge, the process of developing an ontology runs into both of these.
Our expertise has knowledge problems and our discussion of different concepts to
merge uses language to communicate our ideas on this and so the resulting
ontology product may reflect these problems....Of course we may have methods to
resolve these, but shouldn't think that an automatic process. Ontologies
aren't built by tools, but by us using tools.
Yes,
in developing ontologies, whether alone, or collaboratively, we do rely on our
language to describe the meanings that we want to associate with concepts.
But when we are trying to create unambiguous definitions, we tend to use only
that portion of language that is itself fairly unambiguous, and in which the
words are understood as labels for the corresponding concepts that are widely
shared and agreed on -- i.e. we use a fairly precise and common defining
vocabulary, when we want to be precise. The example I provided was the
Longman's Dictionary of Contemporary English, in which the lexicographers
consciously restricted themselves to a fairly small set of about 2000 basic
words (pointers to concepts) to define all of the 56,000 "words and phrases" in
that dictionary. These basic concepts would have to substitute for the
knowledge people must acquire over years of living in the physical world,
bumping into things, getting hungry and eating, interacting with other people,
and learning how to create abstractions and to use the structures of common
physical things and events ("head", "drop") to label more abstract
non-observable derivative concepts . . . they would have to
substitute, *if* they were not also defined and constrained by axioms that
restrict the ways they can relate to each other. As Adam pointed out, when
we add axioms to the concepts in an ontology, the ambiguity is reduced, and the
concepts are no longer merely arbitrary symbols, since they are constrained to
interact with the other symbols in well-defined ways. And as Michael
Gruninger pointed out, the ideal toward which we should try to develop our
ontologies is the case where the axiomatization is sufficiently rich that the
number of models that the definitions are consistent with is only and precisely
those models that we intend to describe. In this way those seemingly
abstract mathematical structures in the computer will have a structure and
behavior closely matching the structure and behavior of those real-world objects
that they are intended to represent, and the computer should be able to do
reliable inferencing about the real world by manipulating those symbols. We do
start with language, but by using the most fundamental and precise and broadly
agreed-on defining words, we can create a comparably precise conceptual defining
vocabulary for the computer, and build up more complex concepts in the computer
that do not have the ambiguity of natural-language words which may be used
in different contexts to label multiple complex aggregates of
concepts.
I
would in fact recommend for the ONTACWG that we adopt an English-language
"defining vocabulary" similar to that used in LDOCE, but for which each word
labels one concept in the existing ontology -- and use only that defining
vocabulary to create the English-language definitions of the concepts we include
in the ontology. Then, when we find that the existing English-language
defining vocabulary does not have the words we need to describe some new concept
that we want to add, we will have a hint that there is some fundamental concept
missing which should be added to the conceptual defining
vocabulary.
The is
another related issue that arises in considering the epistemology of computer
knowledge; people wonder how a computer can get a firm "grounding" in
reality. In the simplest case, a computer may be restricted to doing
in-memory processing of data structures, and the meanings of the data structures
would rely totally on what the programmer intends them to mean, the computer
would have no independent way to check. But the computer
is not totally without connections to the real world. It has disks,
keyboards, and other input/output devices, with which it could "experiment" and
get feedback to verify that there really is some "real world" out
there. And when we reach the stage where the knowledge representation is
sufficiently reliable for basic conceptual computational issues, we could fit
the computer with more elaborate interactive devices to get a more "human"
feeling for the nature of physical reality. To some extent, robotic
systems have to do that right now. But the issues we are dealing with in
this working group don't require that level of "direct physical knowledge" in
the computer. Doing the research to create more elaborate representations
will be, I expect, a lot more efficient after some Common Semantic Model
("COSMO") has been widely adopted, and multiple groups can efficiently share and
reuse the results of their research because it references a common paradigm for
representing knowledge. At that point the efficiency of research may reach
the point where the epistemological issues can be investigated in a meaningful
way. I think the COSMO has to come first.
Pat
Patrick Cassidy
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