Re: SUO: RE: Re: Architecture of an intelligent ontology development algorithm
Tom,
A very sensible summary. The combination that SUMO has taken of top
down (informed by past research in AI, philosophy and logic) and bottom up
(driven by development of numerous domain ontologies, and the WordNet
mapping project) is typical of any large, quality software engineering
project. The "right" approach, if there is one, is certainly a balance of
these extremes.
I also would echo your comments on evolution. An infinite (or even
large) set of ontologies to choose from is not a standard ontology.
This retreads old ground, but it's important for any newcomers to this
list to understand that while the bulk of discussion here is on topics
unrelated to the work of creating an upper ontology, plenty of folks are
quietly building, distributing and using formal ontologies that one day are
likely to become de facto standards, even if largely ignored by this group.
Adam
At 10:39 AM 8/27/2003 -0400, 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 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.
>
>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 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.
>
>Tom
>
>-----Original Message-----
>From: owner-standard-upper-ontology@majordomo.ieee.org
>[mailto:owner-standard-upper-ontology@majordomo.ieee.org]On Behalf Of
>Jon Awbrey
>Sent: Monday, August 25, 2003 5:45 PM
>To: SUO
>Subject: SUO: Re: Architecture of an intelligent ontology development
>algorithm
>
>
>
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>[Reposting after 2 hours]
>
>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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