Talk on Concept Mapping
I recently gave a talk with the above title,
and I put the slides (with some additional
commentary) on my web site:
http://www.jfsowa.com/talks/cmapping.pdf
Following is the extended abstract.
John Sowa
______________________________________________
Concept Mapping
John F. Sowa
VivoMind Intelligence, Inc.
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 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 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.