Re: [ontac-forum] Problems of ontology
JS>> A single, unified upper ontology is impossible to
> >> achieve, and it's not necessary for interoperability.
4. The view of science as a unified body of theory is totally
> unrealistic. Even physics, the hardest of the hard sciences,
> is a hodge-podge of thousands of mutually inconsistent models
> for each area of application. As Bundy said, there is no such
> thing as a unified model of everything.
>
> 5. If physics cannot give us a unified ontology, then how can we
> expect any kind of unity at all when it comes to the squishier
> aspects of life: economics, medicine, business, social sciences,
> etc. In every one of these areas, the best we have are problem
> or task-oriented theories, each of which is based on simplifying
> assumptions that are inconsistent with those of any other areas.
John,
What i found so disturbing about your position, it is not its partiality,
but rather its apparent rejection of the ultimate goal of science led by
ontology, to set an all-comprehensive and unified description of the world.
Humans are beings of numerous desires, but only one of them makes us so
unique and particular and intelligent, the never satiated thirst for
knowledge, driving the mighty minds and intellects from the ancient times.
'All men by nature desire to know', not only particular differences between
things, the province experience, but the underlying causes and fundamental
principles so that to achieve a fundamental and universal learning, and if
not to master but to understand the rules of behavior of this dynamic,
seemingly ever-changing world.
Like all symbols are inherently referential, all knowledge is inherently of
the universal forms, constructs, and general judgments, which the human mind
abstracts from an indefinite number of sense data, from its mental ideation
and communication. The surrounding world has an infinite number of sensible
data, which can be amassed in innumerable databases and catalogs of facts.
And the high goal of science, like physics, to formulate the standard rules
of world behavior as fundamental laws by establishing underlying
regularities, uniformities and correlations between the measured quantities
and properties of individual entities.
So science was initially designed for building the conceptual
representations of objective patterns, laws, and structures, in the forms of
models and schemas, diagrams and graphs, theoretical models and specific
theories, general theories and universal laws. If we agree that 'knowledge
is power', then universal knowledge must be absolute power.
After all, it is the universality of natural phenomena (as the unity of
nature and convertibility of natural forces) inspired Faraday, Maxwell and
Einstein to seek unification of all the physical processes by a single set
of physical laws. It is the universality and completeness of scientific laws
enabled all the variety of modern technological applications. It is the
universality and completeness of the conservation principles, like the
energy conservation and transformation law, make as possible all kinds and
manner of productive applications like energy-conversion systems, from
chemical batteries to thermonuclear fusion reactors. For it is the
conservation law of energy determines all the energy changes from one form
to another: nuclear, radiant, mass, gravitational, kinetic, thermal,
elastic, electrical, and chemical.
Completing the science, the task of ontology is to give us the overall
structures, uniformities, patterns, laws and constraints within which all
the many changes in the world take place, finding out what it is outside and
inside, defining the natures and essences common to all the multitude of
individuals, by classifying the whole universe of things into few
categories, classes, and kinds.
All confusion comes from the lack of understanding that ontology is the
world science, first of all, and that it consists of two complementary
parts: intensional (implicit, underlying, or inherent) component, caring
about the general patterns and structures of the world (exemplified by a
knowledge base schema), and extensional (explicit) component, dealing with a
particular world state (exemplified by all sorts of datasets).
Ontology is thus an account of reality and realities; for it concerns with
the entity-relation types in the world at the first place. At the second, it
studies how the realities (the world things) relate to the concepts and
associations in the mind, to the coded representations and structures in
machines, and to the words and sentences in natural languages.
As an applied ontology, it may be a computing (programming) ontology, a
field of computer science. The computing ontology is promising the developer
not only the tools for organizing information (data) but also the mechanisms
of reasoning over data (strategic rules). So, in its inherent nature,
computing ontology is also a formal representation of reality and its
domains, levels, and complex entities and is used to formulate computable
models, causal algorithms, and reasoning strategies about the world, its
parts and aspects. Being the core classification system and conceptual
schema, it organizes the things in the world into a hierarchy of entity
groupings defined as classes, categories, universals, resources, kinds, or
types. The basic unit of the classification system may be taken as a
category (as in philosophy), a kind (as in empirical sciences), a set (as in
mathematics), a class (as in logic), or a type (as in computer programming).
Currently, ontologies are utilized in information sciences, computing,
artificial intelligence, software engineering, and the semantic web
enterprise as a fundamental form of knowledge and reasoning representation.
Designed to model the domain content in machine-readable, computable forms,
the universe of (discourse of) computing ontologies are narrowly restricted
by formal classes, properties (relationships), or individuals (instances,
members, or tokens), while the world is a hierarchy of distinct kinds of
things organized by distinct types of real relationships.
There are a lot of conferences and workshops devoted to what should be done
with ontology and its content: modeling, construction, extraction,
evaluation, management, alignment, documentation, registry, certification,
usability, interoperability, and applicability in commerce and government.
Though it is clear that the issue of issues is a semantic commensurability
and ontology standardization, where all effort and funding should be
directed at the first place; for without a common standard ontology as a
common code of meanings and rules, there is no base and foundation for the
whole enterprise of ontological semantic technology, or intelligent
applications, where abstract knowledge is to be consubstantiated with
concrete matter.
Bottom line:
Complete ontology is a unified framework ontology, which is an
all-comprehensive model of the things in the world with the real world
semantics (operating with the real referents of signs structures, static or
dynamic).
And what it is not for sure: 'an explicit conceptual model with formal
logic-based semantics', the definition coming as a major obstruction to the
success of the much promising ontological projects as the semantic web and
SUO.
I may suggest that this widely spread misinterpretation could be the reason
of your vigorous resistance to the large goal of a unified ontology,
productive of all intellectual technology and emerging knowledge society
(http://www.eis.com.cy) as much as physics, with its few general principles
and conservation laws, becoming productive of all modern physical
technology.
With respect,
Azamat Abdoullaev
EIS Encyclopedic Intelligent Systems LTD
Pafos, CYPRUS
Moscow, RUSSUA
----- Original Message -----
From: "John F. Sowa" <sowa@bestweb.net>
To: "ONTAC-WG General Discussion" <ontac-forum@colab.cim3.net>
Sent: Tuesday, May 16, 2006 1:32 AM
Subject: Re: [ontac-forum] Problems of ontology
> Ed, Pat, Leo, and Chris,
>
> Thanks for the comments. I'd like to make some brief
> comments on your comments and then discuss one of the
> paragraphs from Bundy's paper.
>
> JS>> A single, unified upper ontology is impossible to
> >> achieve, and it's not necessary for interoperability.
>
> EH> Amen to both parts of the final sentence!
> >
> > ... As stated in a paper I wrote last year, I think there
> > are five major methodologies:
>
> http://www.isi.edu/natural-language/people/hovy/papers/05ICCS-ontol-methods-hovy.pdf
>
> I'm glad you support the main points, and I like your five
> methodologies plus the sixth one you added. My only quibble
> is that I suspect there are a lot more methodologies either
> not mentioned or still to be discovered.
>
> PC> I would argue that every system that successfully interoperates
> > at any level shares some upper ontology, whether explicitly or
> > implicitly.
>
> If you delete the word "upper", I would agree. The point Bundy was
> making is that interoperability on shared data or results is always
> on a problem or task-oriented basis. That is also the point that
> Lenat learned after the 21+ years of working on Cyc: the axioms
> at the upper level are not widely used, the middle-level axioms are
> more important, and for any particular application, the microtheories
> (i.e., the lowest-level axioms) are the most important.
>
> LO> John's and Alan's statements are not without controversy and
> > should not be accepted without disputation however we might be
> > disposed to accept them because they align with our desires.
>
> Even more important than disputation are facts.
>
> LO> I think we are both arguing that we need to embrace more a
> > scientific (and computational) approach to these issues rather
> > than a philosophical approach per se (although of course we are
> > best informed scientifically by being best informed philosophically
> > and not falling into old, well-worn but flawed, ensnaring
> > philosophical arguments).
>
> I agree 100%. As Wittgenstein said, philosophy is primarily
> a systematic presentation of reminders of what we already know.
> Among the facts are the evidence and experience derived from
> 21+ years of Cyc, the 50 years of AI, and the past several
> centuries of reasoning in science and engineering.
>
> For the latter, I'd like to emphasize the following paragraph,
> which I quoted in my first excerpt from Bundy's paper:
>
> As another example, consider the experiment conducted
> by Andreas diSessa on first-year MIT physics students.
> The students were asked to imagine a situation in which
> a ball is dropped from a height onto the floor. Initially,
> the ball has potential but not kinetic energy. Just before
> it hits the floor it has kinetic but not potential energy.
> As it hits the floor it has neither. Where did the energy go?
> The students had trouble answering this question because they
> had idealised the ball as a particle with mass but no extent.
> To solve the problem they had to refine their representation
> to give the ball extent, so that the `missing' energy could be
> stored in the deformation of the ball. Note that this requires
> a change in the representation of the ball, not just a change
> of belief about it.
>
> This is not a philosophical argument. It is a concrete example
> of a typical physics problem, and it illustrates issues that
> scientists and engineers face in every experiment or application:
>
> 1. The formulas and equations for the "upper level" ontology of
> physics -- i.e., the most general formulations of relativity
> and quantum electrodynamics -- are almost impossible to solve
> for any practical problem, and every application has to make
> some approximation based on simplifying assumptions.
>
> 2. Furthermore, the approximations made for one problem involve
> assumptions that are usually inconsistent with the assumptions
> required for some other problem. For example, the ball as a
> rigid body while it is falling, but as a deformable body at
> the instant of impact.
>
> 3. When any problem becomes complex, it nearly always breaks down
> into multiple subproblems that require different assumptions.
> The case of the bouncing ball with just two subproblems is
> extremely simple compared to the enormous number of conflicting
> subproblems involved in designing a bridge or an airplane.
>
> 4. The view of science as a unified body of theory is totally
> unrealistic. Even physics, the hardest of the hard sciences,
> is a hodge-podge of thousands of mutually inconsistent models
> for each area of application. As Bundy said, there is no such
> thing as a unified model of everything.
>
> 5. If physics cannot give us a unified ontology, then how can we
> expect any kind of unity at all when it comes to the squishier
> aspects of life: economics, medicine, business, social sciences,
> etc. In every one of these areas, the best we have are problem
> or task-oriented theories, each of which is based on simplifying
> assumptions that are inconsistent with those of any other areas.
>
> LO> ... stepwise testing of hypotheses against actual reality in
> > the determination and refinement of theory. That's my own form of
> > scientific pragmatism, and one I no doubt imperfectly follow.
>
> I think we all pay lip service to that version of scientific
> pragmatism. The imperfections arise from picking and choosing
> various facts to emphasize as important or to abstract away.
>
> CP> ... a counter-example to the claim that having a common upper
> > ontology across a federation of inter-operating systems is
> > "impossible".
>
> To quote another philosopher, I believe that Peirce shows how global
> commonality can be achieved:
>
> It is easy to speak with precision upon a general theme. Only,
> one must commonly surrender all ambition to be certain. It is
> equally easy to be certain. One has only to be sufficiently vague.
> It is not so difficult to be pretty precise and fairly certain at
> once about a very narrow subject. (CP 4.237)
>
> This quotation summarizes the futility of any attempt to develop a
> precisely defined ontology of everything, but it offers two useful
> alternatives: an informal classification, such as a thesaurus or
> terminology designed for human readers; and an open-ended collection
> of formal theories about narrowly delimited subjects.
>
> In other words, it is possible to have an upper ontology, but only
> if it is "sufficiently vague" -- i.e., denuded of any detailed axioms.
>
> In fact, I would claim that the upper levels should not make *any*
> empirical claims -- the only axioms at the upper levels should be
> true by the definition of the terms involved.
>
> John Sowa
>
> PS: In my previous two notes, I quoted the introduction and the
> conclusion to Bundy's paper. The material in the middle part of
> the paper is covered (with more detail) on the following web site:
>
> http://dream.inf.ed.ac.uk/projects/dor/
> Dynamic Ontology Refinement
>
>
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