SUO: Re: Architecture of an intelligent ontology development algorithm
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Tom Johnston wrote:
>
> Or, perhaps even more generally, the "better question" is how to combine
> intentional, purposive design with natural selection operating on trial
> and error? I think R&D in pharmaceuticals is a good working example
> of that combination.
>
> As to an upper ontology, I do not think that waiting for natural selection,
> operating on a pool of candidates into which some process (analogous to
> genetic mutation) introduces novel candidates or candidate-components,
> is likely to succeed in any time frame meaningful to us.
I think you may be confounding "empirical" (= experience based)
with "empiric" (= snake oil), and I'm pretty sure I never said
such a thing -- though it's you and me against a whole army of
Nobel Prize winners on this one. Oh well, hardly the 1st time.
> I think we need an intentional, purposive design, tested for
> adequacy at each stage of its evolution, against a wide range
> of low level ontologies taken from a diverse set of concerns
> represented by working databases in various areas of academic
> research and business function.
I agree with that. But there are quite a few complications and consequences
yet to be teased out of such a mission statement. By calling attention to the
intentional factor, I am not just trying to point out that we have a purpose here,
but trying to point out that all information is information relative to a purpose
(aim, business, concern, end, goal, intention, interest, object, objective, pragma),
except that the end-in-view is usually so "understood" in each given application
that it becomes invisible and the people so engaged forget to record it as an
explicit parameter. This plays havoc with commensurability when other people
later try to lift info out of context and merge it with other bits of info.
What we have then is a failure to intercommunicate. Just look around.
> Top-down (intentional, theoretical), constantly tested
> and refined by comparison with bottom-up (evolutionary,
> real world).
>
> The testing and refining never stops, of course. So in theory,
> it could lead to revisions in the highest levels of the ontology.
> Only through vacuousness could the highest levels of our ontologies
> become immune to revisionist pressures.
>
> This, of course, is pure Quinean holism. But although, according to Quine,
> even the laws of arithmetic are in principle subject to revision in the face
> of recalcitrant experience, we count on them as being pretty stable. And they
> have been. By the exact same token, I would expect a good upper level ontology,
> once proven stable against a couple of dozen large, robust and successful real
> world databases, to settle down into a stable state.
>
> Nor does the benefit flow in one direction only -- lower level ontologies helping
> with the development of higher level ones by being test cases for their applicability.
> Upper level ontologies can also help us develop better lower level ones, by revealing
> patterns in that lower level data that the originating "subject matter experts" had
> never seen. I provided a brief manufacturing example a week or two ago.
>
> Another set of examples come from generalizing from a set of relational
> tables (or OO classes) to a common supertype table (or class). Several
> vendor-provided "industry standard" data models, such as IBM's banking model,
> define an INTERESTED-PARTY relational table, subtypes of which include CUSTOMER,
> VENDOR, COMPETITOR, REGULATORY-AGENCY. (Of course, this doesn't amount to very
> much, since relational DBMSs support very little of the semantics of super/sub types.
> In fact, in relational databases, they come to nothing more than one-to-one relationships
> between the supertype and each subtype, optional for the supertype, required for the subtype.
> So although the data model diagram with its type hierarchy looks very sophisticated in a
> vendor's slide show, when it gets down to implementation in a working database, it's much
> ado about very little.)
>
> Nonetheless, to summarize: it's top-down and bottom-up, design and trial-and-error.
> If I have any content to add to this truism, it's this: the process should be more
> top-down at the top, more bottom-up at the bottom, but always both, at all levels.
> The influence works both ways. We ontologists have something to add, something
> that reaches all the way down into insurance claims processing databases,
> transportation freight bill reconciliation databases, and grocery store
> shopping basket analysis databases.
In the kinds of situations that I am used to, the database impacts on
a set of research hypotheses, the impact of which we be evaulated by the
logic of hypothesis testing, using standard statistical method. Sometimes
the hypothesis-in-view is set down before the data is gathered; sometimes
a wholesale collection of data is built up as a fund for post hoc hypothesis
making. But it is the logic of inquiry that rules the whole game. One does
not hear people obsessing all that much about "ontology" -- except on really
slow days when they can take an extra half-hour in the cafeteria to wrangle
about what constitutes a "disease entity" or a "valid construct", etc.
> In particular, as I argued in an earlier email about manufacturing databases,
> we should not think that the "real truth" is found in functioning, real world,
> bottom-level databases. The ontologies they embody are often confused, and the
> codebase and user knowledge of the system substantially devoted to compensating
> for the confused ontologies. The whole things are Rube Goldberg (Heath Robinson,
> for the Brits) contraptions, usually and for the most part.
I lost you when you equated "empirical", which I think of as suggesting
a relatively data-driven activity, or something like a Humean model of
science, with random mutation and natural selection. It's a plain
fact that we often have to gather lots of data, theory-laden as
all get out with all sorts of enabling hypotheses, but still
in the absence of a Really Big Theory to account for it.
Sure, "the theory tells us what we can observe", but
that theory can still be falsified under the weight
of accumulating evidence, because not all data is
gathered under the same theoretical blinkers.
The whole point of that analytic/synthetic shell game was that labels
like Up and Down may be useful for local interpretive orientation but
may be impossibe to maintain absolutely globally. The evidence is in,
and our knowledge pottage is canned in epistemological klein bottles.
What I do believe we can talk about more sensibly on a wider scale
is the fact that some models are more globally useful than others,
relative to given objectives. But you have to pin down your goal
before the field of inquiry organizes itself into anything like
a lattice, poset, preorder, or whatever form may be in order.
This leads to a more 3-adic overall structure than the
usual polarities of Top-Dog vs. Under-Dog.
It is my experience that most folks who haven't absorbed
Peirce's critigue of the earlier models of science will
still be gridlocked on the plane of concept vs. data,
instead of being able to integrate the two domains.
But there is the Third Way Out of this morass.
Jon Awbrey
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> Rich,
>
> I would have thought that a fairer summary of what's been said here
> all summer long on many different threads is that there is really no
> such thing as a hypothesis-free algorithm for discovery -- actually,
> that's more like a summary of what's been discovered about discovery
> over the last few thousand summers, but who's counting? So I think
> that a better question might be something along the following lines:
>
> How are concept-driven (analytic, axiomatic, rationalist, top-down) methods
> and data-driven (synthetic, contingent, empiricist, bottom-up) procedures
> best to be integrated in human inquiry, or in the reconstitutions thereof,
> given that the distinction between analytic and synthetic is more relational,
> interpretive, or "situated" than it is absolute, invariant, or "essential"?
>
> A start on answering that question might be to get a better analysis
> of the similarities among and the differences between the various
> types of reasoning that need to be integrated. On that score,
> my advice would be: Read more Peirce.
>
> Jon Awbrey
>
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>
> Richard Cooper wrote:
> >
> > Since many of us seem to agree that a bottom-up
> > algorithm could be used to produce the axiom
> > set of an ontology through situated experience
> > in the real world, I'm trying to draft some
> > requirements for this algorithm.
> >
> > There is a very suggestive paper at
> > http://jasss.soc.surrey.ac.uk/6/3/1.html
> > "Discrete Agent Simulations of the Effect
> > of Simple Social Structures on the Benefits
> > of Resource Sharing".
> >
> > The paper desribes a simulation of agents in an
> > environment somewhat like early human societies
> > are thought to have evolved in.
> >
> > A similar approach could be used to measure the success of
> > each strategy on the basis of how successful agents use that
> > strategy. In a simulated environment, instead of a situated
> > one, its easy to measure behaviors and organize them according
> > to what works well and what doesn't.
> >
> > So in a situated environment, perhaps the algorithm can guess at
> > axioms based on fragments of previous guesses that were successful.
> > The so-called evolutionary algorithms could suggest requirements for
> > monitoring the algorithm's behavior in the real world, measuring success
> > and failure, and buliding a database of experience for process improvement.
> >
> > So it seems to me that the process improvement concepts should be
> > a top level ontology in an algorithm that learns still higher level
> > axioms, while the WordNet concept set provides at least the words for
> > communicating with real world people.
> >
> > Any thoughts on this subject?
> >
> > Thanks,
> > Rich
>
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