# Re: fuzzy string matching

Earl Cox (ecox@metus.com)
Sat, 29 May 1999 18:17:39 +0200 (MET DST)

Fuzzy Logic measures, to some ways, the degree to which "a" is
representative of semantic class A ("Tall men" is a measure of the degree to
which a height "h" is representative of the fuzzy set Tall, or the current
rain fall is representative of Heavy rain, etc.). So a fuzzy string
comparison function fzycmpstr(a,b) might measure the degree to which string
"a" is representative of "b". Now, the cemetery example is OK, but is, in my
opinion, a pathological case, since we can "see" that the two strings are
close to each other in semantic intensionality. However, the operation
fzycmpstr("hat","cat") or ("chair","flair") might tell us the distance
between the two strings, but tells us nothing about their closeness. We have
to ask ourselves what good is the fuzzy comparison "hat", "cat"? I suppose
that depends on the application (such as a spell checker). Even taking a
distance metric and mapping it to a fuzzy set that converts distance into
linguistic variables (so that fzycmpstr("hat","cat") is "very close") still
tells us little about the relationship between "hat" and "cat".

I myself have built fuzzy decision systems (actually a fuzzy Case-based
Reasoning (CBR)Systems, see my article "A Close Shave with Occam's Razor --
The New Face of Fuzzy Logic" in the current issue of PC/AI Magazine) with
semantic nets linking the various objects. The edge of the net contains a
fuzzy membership (similarity) grade, much like the probability measure on a
Markov Chain. I then have functions that use the semantic nets along with
the fuzzy sets defining the underlying case elements to compute fuzzy
degrees of similarity between parameters in the system. However. . . . .

The application of fuzzy logic and fuzzy metrics to non-numeric objects (and
events) has long been a difficult task. One of the few practitioners who has
successfully done this is Bill Siler with his FLOPS fuzzy expert system.
Perhaps we can get bill to wade in on this.

Earl

Will Dwinnell <76743.1740@CompuServe.COM> wrote in message
<#l6ac8Pq#GA.326@nih2naac.compuserve.com>...
>Bruno DiStefano wrote:
>"I feel that you have a starting point. However, you are not
>really using the fuzzy logic in what you show. You are
>trying to determine the "error" of a reading. Yes, I know,
>this is not what you have in mind. However, looking from the
>outside, this is what it looks like."
>
>I submit that from another perspective, this algorithm does
>involve fuzzy logic: the presented algorithm gives a fuzzy
>membership in the class of strings that are "like" the target
>string.
>
>--
>Will Dwinnell

*************************** http://www.metus.com ***************************
Earl D. Cox AUTHOR:
Wandering Epistemologist "The Fuzzy Systems Handbook" (1994)
Phrenologist-for-Hire "Fuzzy Logic for Business and Industry" (1995)
Foreteller of the Past "Beyond Humanity: CyberEvolution and Future
Minds"
(919) 859-1736 (vox) (1996, with Greg Paul, Paleontologist/Artist)
(919) 851-3525 (fax) "The Fuzzy Systems Handbook, 2nd Ed." (1998)
"Fuzzy Tools for Data Mining" (due Summer, 1999)
******* No Good Deed Ever Goes Unpunished (Mark Twain/Abraham Maslow) ******

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