Thanks John,
I would like to follow up on the precision line of thought. As one who
generally likes precision (or at least more than most), I have to admit to
not having achieve it to the extent that would be required to some of the
goals attributed to Ontologies.
Specifically the goals of the automatic and autonomous integration and
adaptation of formally unrelated systems. This generally takes the form of
matching interfaces or data schema. The problem with this goal is that it
rarely achieved by very intelligent and informed people. In general,
compatibility between systems is achieved with interaction, negotiation and
sometimes mutual adaptation. Expecting that a set of specifications are
going to be sufficiently precise, detailed, sufficient and contextual to
allow for autonomous adaptation is hard to accept. If it is possible it
would require GREAT effort do ground all of the specifications. Even after
such effort it is improbable that the adaptation would be TRUSTED without
human inspection and testing.
On the other hand, less formally grounded specifications with some tools
that seem quite possible could AID the process of adaptation, making it one
or 2 orders of magnitude faster and less expensive. For example, if a set
of interfaces were mapped to something like wordnet, it is easy to see how
tools would help the human make connections between interfaces by matching
the concepts. If it were correct 75% of the time - that would be a big win!
Add a little more semantics and you can improve the win, but at some point
there is diminishing returns.
To make this possible the interfaces have to have a common conceptual
framework (which is why we are working on the meta level) as well as the
capability to "ground" domain concepts in ONE OR MORE "hubs", like wordnet
or even Cyc. We can then see a community of tools that would help derive
value from this grounded knowledge The other thing necessary to make this
possible is to accept that models are not perfect and allow statements that
may be partially true or true within a limited context. Precise inference
on imprecise models yields ???
So yes, we have to understand what our value proposition is for various
levels of precision and be realistic about what can be fully automated even
with such the precision control "turned all the way up". (01)
-Cory (02)
-----Original Message-----
From: ontac-forum-bounces@xxxxxxxxxxxxxx
[mailto:ontac-forum-bounces@xxxxxxxxxxxxxx] On Behalf Of John F. Sowa
Sent: Thursday, November 24, 2005 7:46 PM
To: ONTAC-WG General Discussion
Subject: Re: [ontac-forum] Semantic Interoperability: Sowa's Collection
ofModules (03)
Nicolas, Cory, Gary, Jim, Robert, David, Pat, et al., (04)
Jim started this thread by talking about numbers like N
and N-squared, but they get into counting issues before
it's clear what N refers to or what kind of math we
should apply to it. (05)
Nicolas raised an issue that gets closer to the heart
of the matter: (06)
NFR> ... it should be also very clear that the information
> flow ontology for the communication between A and B is
> fundamentally dependent on knowledge of what A and B
> do with that information. (07)
Indeed, there's communication, and there's "what A and B do".
The first depends critically on some language, vocabulary,
and speech acts. The second gets into "doing", which could
mean several things: a logic-based inference, a procedural
computation, a process that triggers physical events, or
just storage for future reference. And both *speech acts*
and *doing* imply some agent who has some purpose -- usually
task oriented -- for saying or doing. (08)
Of all these things, the vocabulary is the most obvious, and
it's the one thing that tends to be the most stable, even if
or perhaps even because the words may have more than one sense.
WordNet makes provision for multiple senses, but many ontology
projects concentrate on a fixed set of senses or types, each
defined by precisely specified axioms. (09)
In science, precision is good because it makes theories easier
to test -- or, as Popper said, *falsify*. But a synonym for
"easily falsified" is "fragile", as we have unfortunately
learned with many of our computer systems. In communication,
some vagueness is often good, because it makes a statement
easier to verify, not falsify. As Peirce said, "It is easy
to be certain, one has only to be sufficiently vague." (010)
Jim raised another point: (011)
JRS> Existing upper ontologies (SUMO, DOLCE, OpenCyc) aren't
> being used much today, so why attempt to build a better one.
> Why not try working with existing ones? (012)
He proposed a test: (013)
JRS> Are there any members of this forum willing to select an
> existing upper ontology and try working with it? If so, I
> suggest they conduct an evaluation, make a selection, and see
> what tests and demonstrations can be run. If they show promise,
> a case can be made for building a better upper ontology. (014)
The biggest ontology ever developed has been tested over a
period of 21 years: Cyc. It was originally supported by
some large companies, each of which invested $500K per year
and assigned their own personnel to test the ontology on
some practical problems. Each company (which included several
government agencies) had full access to the Cyc resources,
including the Cyc developers for assistance and consulting.
There have been some small applications, but no success
stories with a positive ROI. (015)
One problem with Cyc is that the axioms are so precisely defined
that it's fragile. Some people have recommended more formality,
but that would make it even more fragile. There is no evidence
that building a very large ontology, without a major change in
the strategy for use and deployment, would be more successful. (016)
I'm not against precision. We must have it for microprocesors
or spacecraft that go to Saturn. But very precise calculations
and inferences are always narrowly focused and highly specialized.
Even though Cyc may be precise, it is still too general to solve
most of the specialized problems that people need to solve. (017)
We don't even have to look at spacecraft or microprocessors.
Just look at the HALO study, which addressed the task of
answering questions on a freshman chemistry exam. They
tested three projects, including Cyc. Although Cyc started
with the largest predefined ontology in the world, it had
little or no advantage over the other two projects, both
of which achieved a somewhat better score than Cyc. (018)
Gary cited another of Doug Lenat's examples: (019)
GBC> "If it's raining, carry an umbrella." The following are
> assumed in this summary rule... (020)
Then he listed ten assumptions implicit in the rule, such as
"the performer is sane" or "their actions permit them a free
hand (e.g., not wheelbarrowing)". Gary (and Lenat) emphasize
that the number of such variations is open ended. (021)
These problems with Cyc arise in every branch of science,
engineering, or business. The number of possible, but
unlikely exceptions to any rule is so large that the
probability that at least one of them will occur is very
high. That's called Murphy's Law. (022)
Cory discussed the "meta concepts common across architectural
languages and notations" such as "UML, E-R models, OWL,
Collaboration Modeling, Services Interfaces, Information
Models, FEA-RMO, etc." (023)
CC> The approach is to normalize and unify the concepts
> expressed in these various languages into a controlled but
> open set of concepts, this is the "semantic core". These
> concepts may be introduced from any of the architectural
> languages -- our job is to try and "slice and dice" the
> concepts so that the fit together (where possible) and are
> non-redundant (Where possible). We can then describe the
> mapping and/or transformation of various tools and
> representation into this common form. (024)
This classification is very different from the ontologies of
Cyc, SUMO, Dolce, or BFO. Instead of analyzing the content
or subject matter, it addresses the metalevel and analyzes
the kinds of tasks that are performed on that content. This
is orthogonal to the classification of content, but it may be
very important for the applications that use the ontologies. (025)
Although I still believe that further research in ontology is
important, I have little faith in the _Field of Dreams_ slogan:
"If you build it, they will come." Cyc has been built, and the
customers have not come. The major question is what strategies
for designing and deploying ontologies might be more successful.
Following are some points to consider: (026)
1. Standardized vocabularies, terminologies, and nomenclatures
were developed long before computers became available, and
their value has been abundantly demonstrated, even without
formal axioms associated with any of the terms. (027)
2. Many such terminologies have logical errors that must be
corrected. For example, three major links between terms
must be clearly distinguished: type-subtype, type-instance,
and whole-part. Some classifications lump all three under
the heading broader-narrower, but that leads to serious
confusion. (028)
3. Other relationships should also be represented, such
as locationOf, containerOf, attributeOf, and various
relations of geography, kinship, and politics. (029)
4. When two or more terms in the vocabulary have the same
supertype, the differentiae that distinguish them should
be explicitly stated, but very detailed axioms can often
be more of a hindrance than a help. (030)
5. More detailed axioms from science, engineering, law,
philosophy, sociology, etc., are likely to be far too
specialized, theory dependent, and not only unnecessary,
but highly undesirable in a general-purpose ontology.
For example, a general ontology should be neutral with
respect to 3-D or 4-D models of space-time, situation
calculus vs. pi calculus, or continuant-based vs.
process-oriented ontologies. (031)
6. The logic required for the general ontology should be
very simple. Aristotle's syllogisms, which are a subset
of description logics, are sufficient for the definitions
discussed in points #2, #3, and #4 above. More complex
logics should be limited to more specialized microtheories
for particular applications, not for the general ontology. (032)
This outline suggests a major reduction in the complexity of
the logic and highly controversial issues about the nature
of space-time, processes, objects, etc. Those issues may
be extremely important for many purposes, but the fact that
they are controversial means that they should be relegated
to specialized microtheories, not the fundamental framework. (033)
Meanwhile the kinds of metalevel discussions that Cory
mentioned might be able to relate the ontology to software
development processes in a way that Cyc never could.
However, that is another controversial issue that should
not be part of the fundamental framework. (034)
John Sowa (035)
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