A wam implementation for flexible query answering

Advances in Computational …, 2011

Bousi∼Prolog is an extension of the standard Prolog language aiming at to make more flexible the query answering process and to deal with vagueness applying declarative techniques. In this paper we precise a model-theoretic semantics for a pure subset of this language. Next, for the first time, we define a notion of correct answer which provides a declarative description of the output of a program and a goal in the context of a similarity relation. Afterwards, we recall both the WSLDresolution principle and a similarity-based unification algorithm which is the basis of its operational mechanism and then we prove the soundness of WSLD-resolution.

1997

We present a new fuzzy logic programming Language, called LIKELOG, to be used for approximated reat;oning and for fuzzy deductive database applications. The core component of the system is the algori thm of unification, which expands the Martelli-Montanari unification algorithm introducing two similarity relations. The resolution mechanism is a straightforward application of this kind of expanded unification. Given such a fuzzy resolution ruLe we present a fuzzy logic programming system where computed answert; are suppLied wit.h a degree of derivation. The LIKELOG system is more flexible respect to the traditional information systerru;. LIKELOG retrieves answers that are "similar" to the user query. In LIKELOG the user can not only insert queries, hut can also introduce his/ her own relation of similarity, in order to obtain answers that, although approximate, meet hi::i/ her own particular preferenc!:'.:;.

Bio-Inspired Systems: …, 2009

Bousi~Prolog is an extension of the standard Prolog language. Its operational semantics is an adaptation of the SLD resolution principle where classical unification has been replaced by a fuzzy unification algorithm based on fuzzy relations defined on a syntactic domain. In this paper we describe how BousiProlog may contribute to resolve several problems extracted from different application areas, where it is mandatory to deal with vagueness and uncertain knowledge, such as: flexible deductive databases, fuzzy control, fuzzy experts systems, data retrieval or approximate reasoning. Hence, through several (small but meaningful) examples we show the great potential of this programming language.

Flexible Query Answering Systems, 2002

We use the formal model for similarity-based fuzzy unification in multi-adjoint logic programs to provide new tools for flexible querying. Our approach is based on a general framework for logic programming, which gives a formal model of fuzzy logic programming extended by fuzzy similarities and axioms of first-order logic with equality. As a source of similarities we consider different approaches, such as statistical generation of fuzzy similarities, or similarities generated by some information retrieval techniques or similarities arisingfrom ...

PPDP

Bousi∼Prolog is a fuzzy logic programming language with an operational semantics which is an adaptation of the SLD resolution principle, where classical unification has been replaced by a fuzzy unification algorithm based on proximity relations. Hence, it is a programming language well suited for dealing with uncertainty and approximate reasoning. There are several practical applications where Bousi∼Prolog can be useful: flexible query answering; advanced pattern matching; information retrieval where textual information is selected or analyzed using an ontology; text cataloging and analysis; etc. In this paper we give a model-theoretic semantics for a pure subset of this language: we formalize the notion of least fuzzy Herbrand model as the declarative semantics for definite programs. We prove various important properties of these models. Finally we define an immediate consequences operator, which is proved monotonous and continuous, obtaining a fixpoint characterization of the least fuzzy Herbrand model.

2015

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.

dectau.uclm.es

Bousi∼Prolog is an extension of the standard Prolog language aiming at to make more flexible the query answering process and to deal with vagueness applying declarative techniques. In this paper we precise a model-theoretic semantics for a pure subset of this language, we recall both the WSLD-resolution principle and a similarity-based unification algorithm which is the basis of its operational mechanism and then we prove the soundness of WSLD-resolution.

Proceedings of the 10th international ACM SIGPLAN conference on Principles and practice of declarative programming, 2008

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.

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.