SUO: Re: architecture of an intelligent ontology development algorithm
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Tom,
Thankfully, though, we're all agreed that building theories
of things like space, time, & spacetime from scratch is
a total waste of, well, space & time, so now the only
question is what quiet sources shall we seek out
to get some theories ready-made.
Jon Awbrey
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Tom Johnston wrote:
>
> Yeh, pride of authorship is both the engine that drives innovation,
> and a terrible impediment to progress, as well.
>
> -----Original Message-----
> From: Jon Awbrey [mailto:jawbrey@att.net]
> Sent: Wednesday, August 27, 2003 4:45 PM
> To: Tom Johnston
> Cc: Adam Pease; SUO
> Subject: Re: architecture of an intelligent ontology development
> algorithm
>
> o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o
>
> Tom,
>
> There are, of course, many standard ontologies in the academic curriculum,
> though they may not have the meta-tag "ontology" pinned to their gowns yet.
> There are, just in mathematics, standard definitions of things like
> algebras,
> geometries, groups, graphs, ..., ad infinitum, things that qualify as
> "upper"
> if anything ever did, so it's fair to say that "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", and
> those standards will continue to be carried forward solemly and quietly
> without us, but just you try and get these wannabe standardizers to
> recognize what is already standard -- ay, there's the rub.
>
> Jon Awbrey
>
> o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o
>
> Tom Johnston wrote:
> >
> > Adam:
> >
> > I come across quite a few of these ontologies. The ITU, with its M3000
> > series of documents, tried to define a telecommunications-specific upper
> > ontology. I think it failed, in part because it was not abstract enough,
> and
> > the then rapid pace of technological change overtook it. (I think I
> > developed a better one, for a failed start-up company. But that's another
> > story.)
> >
> > In the retail business, the ARTS council (Association of Retail .....
> > something or other) is also in charge of an industry standard data model,
> > i.e. an ontology. I can send a copy (in Erwin data modeling tool format)
> to
> > anyone who's interested.
> >
> > The oil and gas industry also has a standard model.
> >
> > IBM, and probably other vendors, have a banking industry standard model.
> >
> > UCCNET (the group that defines UPCs and bar codes) has something like this
> > too, although they concentrate on defining standard tags (i.e. identifiers
> > for individual things) rather than standard types (i.e. ontological
> > categories).
> >
> > I'm sure a lot of standardization is going on in the academic world, but I
> > thought I'd let everyone know that a lot of standardization is also going
> on
> > in the business world.
> >
> > Tom
> >
> > -----Original Message-----
> > From: owner-standard-upper-ontology@majordomo.ieee.org
> > [mailto:owner-standard-upper-ontology@majordomo.ieee.org]On Behalf Of
> > Adam Pease
> > Sent: Wednesday, August 27, 2003 12:12 PM
> > To: SUO
> > Subject: 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
> > >
> > >
> > >
> > >o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o
> > >
> > >[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
> > >
> > >o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o
> > >
> > >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
> > >
> > >o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o
> o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o
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