  Uncertainty in logic programming has been widely investigated in the last
decades, leading to multiple extensions of the classical LP paradigm. However,
few of these are designed as extensions of the well-established and powerful
CLP scheme for Constraint Logic Programming. In a previous work we have
proposed the SQCLP ({\em proximity-based qualified constraint logic
programming}) scheme as a quite expressive extension of CLP with support for
qualification values and proximity relations as generalizations of uncertainty
values and similarity relations, respectively. In this paper we provide a
transformation technique for transforming SQCLP programs and goals into
semantically equivalent CLP programs and goals, and a practical Prolog-based
implementation of some particularly useful instances of the SQCLP scheme. We
also illustrate, by showing some simple---and working---examples, how the
prototype can be effectively used as a tool for solving problems where
qualification values and proximity relations play a key role. Intended use of
SQCLP includes flexible information retrieval applications.
