Hi John, (01)
Thanks for the article I shall start reading it on the train on my way home
tonight. (02)
Interestingly I get different numbers in with these search orders,
1050 "concept maps" "mind maps" "topic maps"
996 "mind maps" "concept maps" "topic maps"
1040 "topic maps" "concept maps" "mind maps" - and most other conbinations. (03)
All the best,
York. (04)
----- Original Message ----
> From: John F. Sowa <sowa@xxxxxxxxxxx>
> To: [ontolog-forum] <ontolog-forum@xxxxxxxxxxxxxxxx>
> Sent: Tuesday, 15 April, 2008 3:39:31 PM
> Subject: Re: [ontolog-forum] Cmaps (was: (no subject))
>
> York,
>
> Try Google.
>
> > There seems to me a great deal of overlap between Context, Topic
> > and Mind maps. Is this just a naïve conclusion? Is anyone aware
> > of any comparative studies between the three types of mapping
> > techniques and representational elements?
>
> Type the following three phrases in quotes:
>
> "mind maps" "topic maps" "concept maps"
>
> That will give you 1,030 hits that mention all three.
>
> There is also a major question about the degree of formalism.
> In my slides, I discussed the issues of systematically mapping
> informal diagrams to a formal logic:
>
> http://www.jfsowa.com/talks/cmapping.pdf
>
> The intro to the slides (see below) summarizes the issues.
>
> John Sowa
> __________________________________________________________________
>
> Concept Mapping
>
> John F. Sowa
>
> Abstract. The task of knowledge representation has two parts:
> the first is to analyze some body of knowledge and identify the
> relevant concepts, relations, and assumptions; the second is to
> translate the result of the analysis into some notation that can
> be processed by computer. Neither part is easy, but the first is far
> more difficult. Natural languages are capable of expressing anything
> that can be stated in any artificial language with the same level of
> detail and precision, but they can tolerate any degree of vagueness
> during the process of analysis. Artificial languages, such as the
> many variants of symbolic logic, are valuable because they do not
> tolerate vagueness, but what they say so precisely may have no
> relationship to what the author intended. The various notations
> for logic are designed to represent the final precise stage, but
> they provide no intermediate forms that can bridge the gap between
> an initial vague idea and its ultimate formalization. Natural languages
> can represent every stage from the most vague to the most precise, but
> no version of fuzzy logic or related variants can come close to the
> flexibility of natural languages.
>
> The vagueness is not caused by natural language, but by the fact that
> people seldom have a clear idea of what they want to say before the
> analysis has been completed. Engineers have a pithy characterization
> of the phenomenon: "Customers never know what they want until they see
> what they get." Plato's dialogs illustrate the kind of analysis that
> is required. Similar dialogs are necessary when programmers or engineers
> analyze a vague wish list (also called a requirements document) in order
> to generate a formal specification. Those dialogs always take place in
> natural languages, often supplemented with hastily scribbled diagrams,
> but not in any version of logic, fuzzy or precise.
>
> This talk discusses a range of representations from informal to formal
> and compares four notations that are being used in various stages of
> knowledge acquisition, analysis, and representation: the informal
> Concept Maps, the semiformalized Topic Maps, the formal Conceptual
> Graphs, and the formal, but highly readable Common Logic Controlled
> English (CLCE). These and other similar notations have found useful
> niches in the process of analysis and representation, but it is
> important to recognize their different characteristics and areas of
> applicability.
>
> The following slides were presented in the track on Technology,
> Instruction, Cognition and Learning (TICL) at the AERA Conference,
> San Francisco, 10 April 2006.
>
>
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