The aim of this paper is to formally introduce a notion of acceptance and similarity,
based on fuzzy logic, among case features in a case retrieval system. This is pursued
by rst reviewing the relationships between distance-based similarity (i.e. the
standard approach in CBR) and fuzzy-based similarity, with particular attention
to the formalization of a case retrieval process based on fuzzy query specication.
In particular, we present an approach where local acceptance relative to a feature
can be expressed through fuzzy distributions on its domain, abstracting the actual
values to linguistic terms. Furthermore, global acceptance is completely grounded
on fuzzy logic, by means of the usual combinations of local distributions through
specic dened norms. We propose a retrieval architecture, based on the above notions
and realized through a fuzzy extension of SQL, directly implemented on a
standard relational DBMS. The advantage of this approach is that the whole power
of an SQL engine can be fully exploited, with no need of implementing specic
retrieval algorithms. The approach is illustrated by means of some examples from
a recommender system called MyWine, aimed at recommending the suitable wine
bottles to a customer providing her requirements in both crisp and fuzzy way
