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Re: [ontolog-forum] Cmaps (was: (no subject))

To: "[ontolog-forum]" <ontolog-forum@xxxxxxxxxxxxxxxx>
From: "John F. Sowa" <sowa@xxxxxxxxxxx>
Date: Tue, 15 Apr 2008 10:39:31 -0400
Message-id: <4804BE23.3020002@xxxxxxxxxxx>
York,    (01)

Try Google.    (02)

 > There seems to me a great deal of overlap between Context, Topic
 > and Mind maps. Is this just a na´ve conclusion? Is anyone aware
 > of any comparative studies between the three types of mapping
 > techniques and representational elements?    (03)

Type the following three phrases in quotes:    (04)

    "mind maps" "topic maps" "concept maps"    (05)

That will give you 1,030 hits that mention all three.    (06)

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:    (07)

    http://www.jfsowa.com/talks/cmapping.pdf    (08)

The intro to the slides (see below) summarizes the issues.    (09)

John Sowa
__________________________________________________________________    (010)

Concept Mapping    (011)

John F. Sowa    (012)

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.    (013)

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.    (014)

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.    (015)

The following slides were presented in the track on Technology,
Instruction, Cognition and Learning (TICL) at the AERA Conference,
San Francisco, 10 April 2006.    (016)


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