Dear David, (01)
> >> This discussion has been most enlightening... I see language used in
> >> totally chaotic ways with minimal logic & certainly NO universal
> >> logic.
> > MW: Chaotic, yes, but logic is logic whether you use it or not.
> I think I finally get that I'm at one end of the spectrum & logic is
> at the other.
> What I thought I was looking for in these ontology discussions was
> the ability to determine which of 35+ potential meanings a cryptic
> "term" means within a piece of software or documentation. (02)
MW: It can help, but it is not all you need. The constraints that are
formally stated may eliminate some meanings, but you cannot expect logic
alone to give you an unambiguous definition.
> When I'm reading an article in the NYTimes there is surrounding
> context in the article to help guess what an unfamiliar word means.
> When I'm read code, a model or technical documentation, there is
> typically NO additional context to help grok what some token actually
> means. Assuming that I've seen a token before & therefore know what
> it means here is clearly one of the FIRST habits you unlearn when
> working in/around systems. (03)
MW: Right. With systems the important context is the users, and how they use
the system. If a field has a label "weight" but all the users use it for
"age", guess what it actually means.
> When I'm reading the NYTimes I have the benefit of knowing that if
> the article is written by Paul Krugman, that's one context & if by
> Eric Lichtblau an entirely different context. This sort of context
> setting is typically NOT present in systems.
> So what is the precise logic in ontologies actually useful for? (04)
MW: Defining rules that apply to the logical terms in the theory. These
alone may be enough to distinguish between possible interpretations - some
may be inconsistent with the rules - but it is not generally enough to
eliminate all unintended interpretations, that is, logic cannot generally
provide a complete and unambiguous (in linguistic terms) definition.
> I picked up on the ontology wave at several Mitre meetings in the 2004
> + period when increasing the interoperability of data between systems
> was getting a lot of attention.
> To me this is "simply" a systems maintenance issue. If you know what
> the system is doing & what data it manipulates, then it's (at least
> theoretically) easier to interoperate with other systems.
> How do ontologies make this-VERY expensive-process easier? (05)
MW: Actually, I think it is as much philosophical ontology, or more
particularly its application, that helps here. Philosophical ontology asks
the questions about what sorts of things there are. This gives a framework
for analysis (there are several possible frameworks) and it is a matter of
analysis to try to work out how what is going on in a system fits into this
ontological framework, taking into account both what is formally defined in
a system, plus information about how it is used. Analysing several systems
against a single ontological framework enables you to (more easily)
integrate the data from them.
> I would further observe that software applications have largely
> evolved along a rather chaotic path, particularly when one looks at
> the language used inside the systems to label he data being used. In
> a chaotic environment (e.g a collection of 100s/1000s of applications
> written & maintained over decades for constantly evolving business
> needs) how does applying organized logic (single term, single
> meaning) help with this situation? (06)
MW: That is the discipline you need to apply in your integrating ontology.
> I am saying that if I can easily know AMT means "amount" (which also
> carries situational specifics) in Context A & in Context B AMT means
> "Amtrak" that would be a HUGE leap forward. (07)
MW: You need to discover this by analysis, including asking the users. (08)
MW: In the oil and gas industry where I used to work we had this problem
with engineering data, multiple applications are used in the design of, e.g.
offshore oil rigs that cost billions of dollars, and that data needs to be
repurposed so that they can be operated and maintained safely. We developed
an ISO standard data model plus standard reference data (ISO 15926) as an
ontology to support this. It has been a mammoth undertaking reflecting the
true difficulty of achieving this. But where it has been applied it has
delivered significant benefits. And no, it was not being used on Deep Water
> >> Most "documents" are stuff written to be read by other humans...
> >> often corrected by highly skilled humans called editors to make
> >> better sense.
> >> I know this is crazy, but I consider software-Algol, Jovial, COBOL,
> >> Fortran, Java, Objective-C, C, C++, PHP, etc-programs/scripts/code to
> >> be documents.
> > MW: Perfectly reasonable.
> Perfectly reasonable what?
> Software is NOT a document? Or software IS a document? (09)
MW: Of course it is a document. (010)
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