In some background research I ran across:
One of the most important applications of fuzzy set theory is the concept of linguistic variables. A linguistic variable is a variable whose values are not numbers, but words or sentences in a natural or artificial language. The value of a linguistic variable is defined as an element of its term set? a predefined set of appropriate linguistic terms. Linguistic terms are essentially subjective categories for a linguistic variable.
Linguistic terms do not hold exact meaning, however, and may be understood differently by different people. The boundaries of a given term are rather subjective, and may also depend on the situation. Linguistic terms therefore cannot be expressed by ordinary set theory; rather, each linguistic term is associated with a fuzzy set. (“Soft sets and soft groups,” by Haci Akta? and Naim Ça?man, Information Sciences, Volume 177, Issue 13, 1 July 2007, Pages 2726-2735
Fuzzy sets are yet another useful approach that has recognized linguistic uncertainty as an issue and developed mechanisms to address it.
What is “linguistic uncertainty” if it isn’t a question of “subject identity?”
Fuzzy sets have developed another way to answer questions about subject identity.
As topic maps mature I want to see the development of equivalences between approaches to subject identity.
Imagine a topic map system consisting of a medical scanning system that is identifying “subjects” in cultures using rough sets, with equivalences to “subjects” identified in published literature using fuzzy sets, that is refined by “subjects” from user contributions and interactions using PSIs or other mechanisms. (Or other mechanisms, past, present or future.)
[…] categories. That makes natural language processing difficult. But the problem is being addressed. Patrick Durusau cites an article[1] that suggests linguistic variables cannot be members of ordinary sets, that […]
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