Re: Order & meaning in a proposition

"Lemming" <thiswillbounce_at_bumblbee.demon.co.uk> wrote in message news:3cs5701a7v0onhm11khmcjf154qakn6vk6_at_4ax.com...
> On Tue, 6 Apr 2004 13:00:55 -0500, "Dawn M. Wolthuis"
> <dwolt_at_tincat-group.com> wrote:
>
> >"Lemming" <thiswillbounce_at_bumblbee.demon.co.uk> wrote in message
> >news:gjo570h5osc09f9mab31picaml0fiaco28_at_4ax.com...
> >> On Tue, 06 Apr 2004 17:57:56 +0100, Lemming
> >> <thiswillbounce_at_bumblbee.demon.co.uk> wrote:
> >>
> >> >The point of contention seems to be that since the President was
> >> >mentioned in the statement before the Secretary of the Interior, then
> >> >the President must have been seated first. It could simply be though
> >> >that the writer felt that the President is more important than the
> >> >Secretary, and so should be mentioned first. The writer need not even
> >> >have known the order of seating in order for the statement to be
> >> >written exactly as is.
> >>
> >> D'Oh, I get it now. Because a statement could have multiple
> >> interpretations, when we model it we risk losing one or more of those
> >> interpretations.
> >
> >Close, very close
>
> Believe me, I'm glad to have even got close!
>
> >- it is not just when we model it, but depending on how we
> >model it -- we can lose more with one model than another. Data models
are
> >important for being able to apply predicate logic for querying the data,
for
> >example. But a data model that captures the ordering of compound nouns
in a
> >proposition retains more information (even if not obviously more data)
than
> >one that randomly orders the nouns.
>
> I'm curious what modelling methods retain sufficient information that
> such nuances are captured in the final model. Do any such methods
> exist?

I'll take that bait -- if one were to model data as if for an XML document, for example, then you would not have to put the data in 1NF and could retain "multivalues" in your model. First normal form is perhaps the most obvious way thast the relational model loses such information (as ordering) when modeling propositions by splitting them into so many propositions. This is the same way we modeled data in the 70's (we as a profession) when storing it in indexed sequential files with tables permitted as fields in records. It is the same way data is modeled in PICK and pretty much every data model, I suspect, other than relational. So, start by tossing out 1NF and that will make a big difference in this area in my opinion. Cheers! --dawn

> Lemming
> --
> Curiosity *may* have killed Schrodinger's cat.
Received on Tue Apr 06 2004 - 20:32:28 CEST