I am resending this, having got no response previously (except from my Human
Factors people), and people are paying me good money to solve this
(subcontracts are likely to be small, and consequently UK based
organizations preferred). (01)
The human factors view of situational awareness would also reinforce
this (Mike Bennet's) view. Important information is often conveyed at the
level, but only provided the sender and receiver have a common
understanding (use the same business process). Repurposing the
information requires that one infers the perception level terms from
comprehension level terms and the business process. This is an area
where I'm looking for some input, and I've inserted below is a statement
of the problem. Anyone have any good ideas of research that I can draw
The General Problem: The area of interest is the definition of metadata
which will allow users to filter and search for various sorts of imagery
and sensor records. The annotation of the records is frequently done by
specialists, often under time pressure and often with a specific task in
mind. The resulting annotation may contain "summary elements", that is
terms that are chosen from a comprehension vocabulary, rather than a
perception vocabulary. The problem of interest is, knowing the task the
information was collected for, can we infer the perception elements for
the data? These would then be used directly as search criteria or as
inputs to a new summarization procedure. (04)
Background: The starting point for this work was the development of
incident codes for the EU project OASIS, which was concerned with shared
situation awareness and how situation data for a major emergency could
be shared across organizational and national boundaries. Analysis of
incident codes from just a few organizations (mainly UK fire and police
services) showed little consistency, with some codes being applicable in
only a single area. It was noted that the codes were correlated to the
roles and responsibilities of the particular organization, and that,
from a human factors viewpoint, these were comprehension level codes,
that is codes based on both an understand of the factors of the
situation and an understanding of the role of the organization using the
A human factors analysis of situation awareness typically separates out
three levels of awareness:
* Perception of the elements of the situation;
* Comprehension of their meaning;
* Projection of their (future) significance.
It seems likely that the transition from perception level to the
comprehension level can be modelled as a knowledge based process in
which certain perception level elements (or combinations of elements)
are emphasised while others are neglected, producing a summary
(comprehension level) code, depending of the requirements of the user.
For example, a fire service will have a specific code for a fire in a
thatched cottage, as this will require six fire engines rather than the
usual two. By contrast, it such a distinction may not be needed by an
ambulance crew. (06)
Ontology Engineering: The study is under the domain of ontology
engineering, that is defining a specific ontology to meet a business
requirement, as opposed to other ontology studies, such as the formal
properties of an ontology. The working assumption is that the ontology
will be engineered to match the decision making processes in which it is
used. For example, the fact that a subject swims and eats fish is not
used to distinguish a manager from a scientist, but it is used to
distinguish a penguin from an ostrich. (07)
Factors that one may consider in engineering an ontology include:
* Usability - is the observer likely to be able to distinguish a
song thrush from a fieldfare?
* Reliability - can the observer reliably distinguish the shades
"taupe" and "linen"?
* Risk - are the effects of a mis-identification significant?
* Cognitive level - is the term at the perception or the
comprehension level? (08)
The Problem in Detail: In a closed environment, where all actors
understand comprehension level terms, these allow faster communication
and a more consistent approach. In an open or semi-open environment,
where information may be repurposed, comprehension level terms are
likely to be uninformative or even incomprehensible. In particular, to
search for data, users may not be aware of the comprehension level terms
and may need to search at the perception level. (09)
What we are looking for are:
* Descriptions of how perception level terms are used to derive
comprehension level terms;
* Methods for inferring perception level terms from comprehension
* Method for identifying what perception information is lost when
decomposing a comprehension level term, so that failure to find a match
is not taken as confirmation of absence. (010)
Sean Barker (012)
Bristol, UK (013)
[mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx] On Behalf Of Mike
Sent: 27 April 2009 14:52
Subject: Re: [ontolog-forum] Digital Ontology and digital ontology (014)
*** WARNING *** (015)
This message has originated outside your organisation,
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Keep this in mind if you answer this message. (016)
I think you are right. If an ontology is intended to provide a meangful
record of concepts for some useful purpose, then the underlying nature
of reality is of less relevance than the reality as seen by the
system(s) that the ontology applies to. (018)
I would also suggest that, while it may be interesting to have an
ontology of concepts as humans see them, this is less important than
having ontologies of concepts of relevance to business or other
application domains that computers are involved with. I think we need to
learn to walk before we can fly. As I see it, there is a glaring hole in
many systems application developments, whereby we need a technology
neutral, business-reviewable model of the terms that systems, data
models and message models are developed against. In the messaging
standards world, and in a distressing number of systems developments,
the design is managed from the logical or even the physical design
level, resulting in brittle applications and insufficient business
oversight. For instance you will not find a good business model of a
credit default swap, but you will find data models of these lurking in
some technical design, which may or may not accurately reflect the
business knowledge of what one is. We rely instead on some highly paid
technical genius to understand and model these. This is very poor from a
technical quality assurance point of view. (019)
So ontologies have huge potential to plug the gap in technical
development and quality assurance. This means developing a good
technology development methodology, based on the Zachman Framework, in
which the "Semantic model" section is fulfilled by an ontology. (020)
To do this, the ontology does not need to define the physics underlying
the different concepts except where it is a physics ontology. Nor in my
humble opinion, does it need to account for all the concepts that relate
to the sensory inputs of a human brain. Rather, we need to look at the
sensory inputs of a business - legal, accounting, reputation and so on,
and model the terms that have meaning relative to those. (021)
There are plenty of advanced and interesting things that can be done
with ontology beyond this, but I would suggest that ontologies which
deliver business meaning are more immediately relevant than all those
cool things. We have the opportunity to plug a very serious gap which is
pretty easy to plug. (022)
John F. Sowa wrote:
> Paola, Azamat, Christopher, and Mike,
> PDM> But some passages in the lecture seem to create the impression
> > that physical world results from a certain kind of computation...
> I agree. And that's a metaphor that is helpful to a certain extent,
> but it also has some connotations that may be more distracting than
> Peirce's semiotics is even more general than computation, since every
> kind of computation processes signs. But the idea of signs also has
> connotations that can be distracting. For such reasons, I think it's
> important to use multiple ways (or paradigms) for describing the same
> phenomena in order to emphasize what is common beneath all the
> terminology and metaphors.
> AA> But there are noted physicists, who could see the things i
> mentioned... S. Weinberg [24 October 2002, "Is the Universe a
> Computer?" The New York Review of Books].
> Thanks for the reference. I found the full article on the web:
> I agree that the claim that the universe can be adequately modeled as
> a cellular automaton is dubious, but I'd like to quote another point
> from Weinberg's review:
> SW> The central theme of the book is easily stated. It is that many
> > simple rules can lead to complex behavior. The example that is >
> used repeatedly to illustrate this theme is a favorite toy of >
> complexity theorists known as the cellular automaton...
> I believe that central theme is important. But I'd also like to add
> that the traditional continuous mathematics used in physics also leads (024)
> to enormous complexity. Newton's simple equation F=ma leads to and
> explains very complex kinds of systems. The carbon atom combined with (025)
> a dozen or so other kinds of atoms leads to the enormous complexity of (026)
> organic molecules, DNA, and life.
> I think that Weinberg makes many important observations, but I
> strongly disagree with his concluding sentence:
> SW> In the study of anything outside human affairs, including the
> > study of complexity, it is only simplicity that can be interesting.
> The only things that people can observe and act upon are extremely
> complex systems. It took thousands of years of civilization to
> discover those simple equations of theoretical physics (or those
> simple cellular automata). But people still see, feel, and think
> about those complex things and events.
> AA> The prolix volume mentioned was just a good instigation to view
> > the similarities and differences of two types of ontology.
> It's important to recognize those differences, but the issues
> discussed by Wolfram and Weinberg are very far from the central focus
> of the ontologies discussed in this forum.
> All the ontologies we have been considering focus on complex things
> and events that people see and talk about. They deliberately ignore
> issues in the foundations of physics and the universe, either from a
> digital or an analog point of view.
> CS> I think [Wolfram] does in fact claim that the real world has
> > a certain underlying simplicity.
> I agree, but he also talks about the complexity that arises from that
> simplicity. For the kinds of ontologies we have been discussing, the
> central focus is complex things and events. Any simulations or
> foundations in either quantum mechanics or cellular automata are very
> far removed from the focus of those ontologies.
> CS> But his work is about how apparently real-enough complexity
> > can be produced by simple automata. And on NKS p469 he does say > (027)
> > "But it does mean that if one once discovers a rule that
> > reproduces sufficiently many features of the universe, then
> > it becomes extremely likely that this rule is indeed the final
> > and correct one for the whole universe."
> I agree that any such rule would be very interesting. But it would
> have almost no effect on the ontologies such as Cyc, SUMO, BFO, Dolce, (028)
> or any of the others we have been discussing.
> MB> I thought that the idea that the complexity of the real world
> > can arise from very simple patterns had been well explored by >
> Holland and others in the "complexity" world. Surely that's no >
> longer a contentious point...
> I agree. But the kind of simplicity that Wolfram and Weinberg are
> searching will have little or no effect on the ontologies discussed in (029)
> this forum. Even if they discovered the magic rule that governs the
> entire universe, the application of that rule would involve an immense (030)
> amount of computation before it could explain anything that we see
> every day.
> But there are some very important lessons we can learn from those
> 1. No ontology that has been proposed in this forum adequately
> addresses the fundamental principles (whether digital or
> analog) that govern the universe.
> 2. The central focus of our common ontologies has been the kinds
> of things and events we experience every day. Those things
> are immensely more complex than the simple foundations of
> theoretical physics (whatever they may be).
> 3. The categories in our ontologies are at best useful descriptions
> and approximations of commonsense phenomena. They are not and
> cannot be considered the ultimate foundations of everything.
> 4. No currently available ontology has proved to be adequate for
> describing everything that people talk about and write programs
> to process, but they have been useful for many purposes.
> 5. Therefore, our standards for ontology should support and relate
> an open-ended variety of specialized ontologies for different
> purposes. No single one of them can or should be considered
> the foundation for everything that we talk about or need to
> process on our computer systems.
> John Sowa
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