Re: nature -> "human brain" -> "language terms" ==>> knowledge ?
There is nothing wrong with your method Alexander.
If it is of your own devising you are to be congratulated, because it seems to
be basically "distributional analysis", which is a key Machine Learning
technique (and one which revolutionized linguistics in the... 30's?)
The problem, and I think Gary is saying this too, is that you will find lots
of hierarchies, and they will be incompatible with each other. None of them
will capture all the information which "reflects on nature." No one order
will be enough.
That is the problem: not how we find order, but the amount, and the nature
(inconsistent), of the order we find.
I think the solution is to do just the kind of search you propose, but to do
it "on the fly." So you can find the "meaning", out of all the
(inconsistent!) "meanings" with which it is possible to interpret the world,
which is relevant to a given problem at any given moment.
-Rob
On Wednesday 16 March 2005 02:43, Alexander Povolotsky wrote:
> Hello,
>
> I follow the concept that the human brain reflects on "nature"
> (as an objective reality). In its turn, the above reflection
> gets "captured" into the language terminology in some "raw" form.
>
> Below I propose the method of retrieving this "raw
> knowledge", being captured in the language terminology, via
> the means of the hierarchical classification of all terms
> (nouns - see below), contained in the given human language
> (say English Language as most scientifcally common).
>
> The suggested approach is based on the verb <-> noun grouping
> and is "stolen" by me from the Software Object Oriented
> representation of the class (object). In this particular
> adaption of the OO, the terms (nouns) are analogous to the
> object's data and the verbs are analogous to the "methods"
> (aka "member-functions") which could be applied to (performed on) the
> data.
>
> Suppose for each term (noun) avalable in the language, we
> will "gather" the set of all verbs, which could be applied to
> the given term (noun).
>
> Each set is corresponding to the unique noun, as it was
> described above - so the trees are built around nouns due to
> their one-to-one unique relationship with the given specific
> set of verbs.
>
> Then we could compare each generated (per above description) set
> against all other sets (separately on one-to-one basis) to find
> whether some sets of verbs could share the "common" subsets.
>
> Then we could attempt to detect whether some sets were
> derived (inherited) from the other sets so we would be able
> to build the hierarchical trees of such related sets.
>
> Actually, the nodes of the trees should contain the nouns
> (rather than corresponding sets of their verbs).
>
> The top node of each such tree would contain the set (actually
> uniquely corresponding to it noun as mentioned above), which would contain
> just the "common" subset of the verbs or the "minimum" number of verbs.
> Such noun with the "minimum" set of verbs has the highest
> level of the "abstraction" in the given tree.
>
> To complicate the "picture" the two nodes, which belong to
> two different trees, may "act" as "parents" nodes to generate
> the "child" node (Multiple Inheritance), etc.
>
> Further, some quantification of the "abstraction" value could be
> applied to each distinct tree - the "most bottom" node should
> have the "abstraction value" set to 0 (zero) and for each
> next higher level the "abstraction value" should be
> incremented by 1 (one).
>
> The above excercise, which I am suggesting to conduct - is not intended to
> develop some aid in processing human texts, except may be for the very
> small minor thing of being able to somewhat quantatively
> characterize the average level of abstraction, used in the text (and
> only if the text is recognized, using other cognitive means, as
> "scientific", covering certain domain(s) of the science). I "claim",
> however, that the terminology, developed historically in the > human
> language, "subconciously" reflects (without much of "distortion"/"noise")
> the objective fundamental reality of the
> nature, so the language's terms are in fact "synonyms" of the
> nature's attributes. So in that sense I believe my excercise could
> contribute to studying of the nature itself and its universal
> structure. I guess that the results obtained (as described above)
> could be analyzed using methods developed in the Theory of Graphs.
>
> Below is my view on classification of the knowledge :
>
> Currently human race uses two major levels of expressing the knowledge:
>
> 1) Textual expression - historically it was developed/used first,
> but it is rather "primitive": very context related, non-
> quantative, potentially being subject of uncertainties of
> interpretation(s), could be "false" in principle - hence the
> problems being dealt with,
> in the scientific application domain, (objectively speaking, this
> not a fundamental but application domain). This textually
> expressed knowledge is "acceptable" for the human
> specific utilitarian domains. However, when the textual knowledge
> addresses the fundamental areas/domains - it is (in my view)
> should be considered to be "preliminary", subject of "upgrading"
> into the mathematically expressed knowledge (see below).
>
> 2) Mathematically expressed knowledge (formulas) is: precise,
> quantative,(typically) proven by (based upon quantative)
> experimental data. This form of expressing the knowledge is used
> for describing the fundamentals of the nature (which is not
> necessary utilizable by humans and, by all means, often could not
> be utilized by the humans immediately). Theoretically, this
> mathematically expressed knowledge could "survive" beyond the
> "era" of the human race existence (dialectically speaking,
> everything starts, evolves and ends/gets "pre-empted").
>
> Thanks,
> Best Regards,
> Alexander Povolotsky