Bousi~Prolog: a Prolog Extension Language for Flexible Query Answering

Julián-Iranzo, Pascual; Rubio-Manzano, Clemente; Gallardo-Casero, Juan

doi:10.1016/j.entcs.2009.07.064

Cited by 21 publications

(13 citation statements)

References 17 publications

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“…Neither LIKELOG nor SiLog are publicly available, what prevent a real evaluation of these systems, and they seem immature prototypes. In this same line of work, Bousi∼Prolog [12,15], on the other hand, is the first fuzzy logic programming system which is a true PROLOG extension and not a simple interpreter able to execute a weak SLDresolution procedure. Also it is the first fuzzy logic programming language that proposed the use of proximity relations as a generalization of similarity relations [11].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees

Julián-Iranzo

Moreno

Penabad

et al. 2015

Electron. Proc. Theor. Comput. Sci.

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FASILL (acronym of "Fuzzy Aggregators and Similarity Into a Logic Language") is a fuzzy logic programming language with implicit/explicit truth degree annotations, a great variety of connectives and unification by similarity. FASILL integrates and extends features coming from MALP (Multi-Adjoint Logic Programming, a fuzzy logic language with explicitly annotated rules) and Bousi∼Prolog (which uses a weak unification algorithm and is well suited for flexible query answering). Hence, it properly manages similarity and truth degrees in a single framework combining the expressive benefits of both languages. This paper presents the main features and implementations details of FASILL. Along the paper we describe its syntax and operational semantics and we give clues of the implementation of the lattice module and the similarity module, two of the main building blocks of the new programming environment which enriches the FLOPER system developed in our research group.

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Section: Introductionmentioning

confidence: 99%

A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees

Julián-Iranzo

Moreno

Penabad

et al. 2015

Electron. Proc. Theor. Comput. Sci.

Self Cite

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“…Instead of classical syntactic unification, we speak now about weak unification [12]. Of course, the set of similarity equations is assumed to be safe in the sense that each equation connects two symbols of the same arity and nature (both predicates or both functions) and the properties of the definition of similarity relation are not violated, as occurs, for instance, with the wrong set {eq(a, b) = 0.5, eq(b, a) = 0.9} which, in particular, it does not satisfy the symmetric property.…”

Section: Similarity Relations and Fuzzy Logic Programmingmentioning

confidence: 99%

“…This approach is followed, for instance, in the fuzzy logic languages LIKELOG [10] and BOUSI∼PROLOG [12], where a set of usual PROLOG clauses are accompanied by a set of similarity equations playing an important role at (fuzzy) unification time. Instead of classical syntactic unification, we speak now about weak unification [12].…”

Section: Similarity Relations and Fuzzy Logic Programmingmentioning

confidence: 99%

“…Once this process has finished, the tool completes the intended similarity relation by performing the reflexivesymmetric-transitive closure according to Algorithm 1 (that we have just implemented in PROLOG) which is inspired by the one described in [29], [12], but generalizing it in order to deal with (multi-adjoint) lattices beyond the [0, 1] case 2 :…”

Section: A Syntax For Similarity Filesmentioning

confidence: 99%

“…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]). …”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Logic Rules Modeling Similarity-based Strict Equality

Moreno

Penabad

Vázquez

2014

Annals of Computer Science and Information Systems

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Abstract-A classical, but even nowadays challenging research topic in declarative programming, consists in the design of powerful notions of "equality", as occurs with the flexible (fuzzy) and efficient (lazy) model that we have recently proposed for hybrid declarative languages amalgamating functional-fuzzylogic features. The crucial idea is that, by extending at a very low cost the notion of "strict equality" typically used in lazy functional (HASKELL) and functional-logic (CURRY) languages, and by relaxing it to the more flexible one of similarity-based equality used in modern fuzzy-logic programming languages (such as LIKELOG and BOUSI∼PROLOG), similarity relations can be successfully treated while mathematical functions are lazily evaluated at execution time. Now, we are concerned with the socalled Multi-Adjoint Logic Programming approach, MALP in brief, which can be seen as an enrichment of PROLOG based on weighted rules with a wide range of fuzzy connectives. In this work, we revisit our initial notion of SSE (Similarity-based Strict Equality) in order to re-model it at a very high abstraction level by means of a simple set of MALP rules. The resulting technique (which can be tested on-line in dectau.uclm.es/sse) not only simulates, but also surpass in our target framework, the effects obtained in other fuzzy logic languages based on similarity relations (with much more complex/reinforced unification algorithms in the core of their procedural principles), even when the current operational semantics of MALP relies on the simpler, purely syntactic unification method of PROLOG.

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The Role of Synonymy and Antonymy in ’Natural’ Fuzzy Prolog

Sobrino

2011

Soft Computing in Humanities and Social Sciences

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Bousi~Prolog: a Prolog Extension Language for Flexible Query Answering

Cited by 21 publications

References 17 publications

A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees

A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees

Fuzzy Logic Rules Modeling Similarity-based Strict Equality

The Role of Synonymy and Antonymy in ’Natural’ Fuzzy Prolog

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