A Transformation-based Implementation for CLP with Qualification and Proximity

TL;DR

This paper introduces a transformation technique and a Prolog implementation for SQCLP, an extension of CLP that incorporates qualification values and proximity relations to handle uncertainty and similarity in logic programming.

Contribution

It provides a method to convert SQCLP programs into standard CLP programs and demonstrates a practical Prolog prototype for applications involving uncertainty and proximity.

Findings

Transformation from SQCLP to CLP is semantically correct.

Prolog prototype effectively handles qualification and proximity-based reasoning.

Examples show practical utility in information retrieval tasks.

Abstract

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.