Similarity-based reasoning in qualified logic programming

Abstract

Similarity-based Logic Programming (briefly, SLP ) has been proposed to enhance the LP paradigm with a kind of approximate reasoning which supports flexible information retrieval applications. This approach uses a fuzzy similarity relation R between symbols in the program's signature, while keeping the syntax for program clauses as in classical LP . Another recent proposal is the QLP (D) scheme for Qualified Logic Programming, an extension of the LP paradigm which supports approximate reasoning and more. This approach uses annotated program clauses and a parametrically given domain D whose elements qualify logical assertions by measuring their closeness to various users' expectations. In this paper we propose a more expressive scheme SQLP (R, D) which subsumes both SLP and QLP (D) as particular cases. We also show that SQLP (R, D) programs can be transformed into semantically equivalent QLP (D) programs. As a consequence, existing QLP (D) implementations can be used to give efficient support for similarity-based reasoning.

A declarative semantics for CLP with qualification and proximity

“…We close this subsection with a brief discussion on the relationship between the entailment relation D,C used in this report and a different one that was proposed in (Caballero et al 2008) and noted S,D . In contrast to D,C , the entailment S,D depended on a given similarity relation S. In the context of the SQCLP scheme, one could think of an entailment S,D,C depending on S and defined in the following way: given two qc-atoms ϕ and ϕ , we could say that ϕ (S, D, C)-entails ϕ (in symbols, ϕ S,D,C ϕ ) iff ϕ : A d ⇐ Π and ϕ : A d ⇐ Π such that there is some substitution θ satisfying S(A , Aθ) = λ = b, d λ, d d and Π |= C Πθ.…”

“…The Bousi∼Prolog language (Julián-Iranzo et al 2009;Julián-Iranzo and Rubio-Manzano 2009b;Julián-Iranzo and Rubio-Manzano 2009a) has been designed with the aim of generalizing SLP to work with proximity relations. A different generalization of SLP is the SQLP scheme (Caballero et al 2008), designed as an extension of the QLP scheme. In addition to clause annotations in QLP style, SQLP uses a given similarity relation S : S × S → D (where D is the carrier set of a parametrically given qualification domain) in order to support flexible unification.…”

A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees

“…In QLP a qualification domain D is associated to a program and their rules annotated with qualification values, resulting a parametric framework: QLP(D). In [5] they introduce similarity relations in their QLP(D) framework by adopting a transformational approach. The new Similarity-based QLP(D) scheme, named SQLP(D), transforms a similarity relation into a set of QLP(D) rules able to emulate a unification by similarity process.…”

Fuzzy Logic Rules Modeling Similarity-based Strict Equality

“…Most of these languages implement (extended versions of) the resolution principle introduced by Lee [4], such as Elf-Prolog [5], Fril [6], F-Prolog [7] and MALP [8]. There exists also a family of fuzzy languages based on sophisticated unification methods [9] to cope with similarity/proximity relations, as occurs with LIKELOG [10], SQLP [11] and BOUSI∼PROLOG [12], [13] (some related approaches based on probabilistic logic programming can be found in [14], [15]). …”