Fuzzy Logic Programming Language Research Articles

Tuning fuzzy SPARQL queries

During the last years, the study of fuzzy database query languages has attracted the attention of many researchers. In this line of research, our group has proposed and developed FSA-SPARQL (Fuzzy Sets and Aggregators based SPARQL), which is a fuzzy extension of the Semantic Web query language SPARQL. FSA-SPARQL works with fuzzy RDF datasets and allows the definition of fuzzy queries involving fuzzy conditions through fuzzy connectives and aggregators. However, there are two main challenges to be solved for the practical applicability of FSA-SPARQL. The first problem is the lack of fuzzy RDF data sources. The second is how to customize fuzzy queries on fuzzy RDF data sources. Our research group has also recently proposed a fuzzy logic programming language called FASILL that offers powerful tuning capabilities that can accept applications in many fields. The purpose of this paper is to show how the FASILL tuning capabilities serve to accomplish in a unified framework both challenges in FSA-SPARQL: data fuzzification and query customization. More concretely, from a FSA-SPARQL to FASILL transformation, data fuzzification and query customization in FSA-SPARQL become FASILL tuning problems. We have validated the approach with queries against datasets from online communities.

Seeking a safe and efficient similarity-based unfolding rule

The unfolding transformation has been widely used in many declarative frameworks for improving the efficiency of programs. Inspired by our previous experiences in fuzzy logic languages that do not deal with similarity relations, in this work we try to adapt such operation to the framework of the fuzzy logic programming language FASILL (acronym of “Fuzzy Aggregators and Similarity Into a Logic Language”) which has been developed in our research group for coping with implicit/explicit truth degree annotations, a great variety of connectives and unification by similarity. The traditional unfolding transformation is based on the application of unifiers on the heads and computational steps on the bodies of program rules. However, when considering similarity relations, the premature generation and application of weak (similarity-based) unifiers at unfolding time could destroy the correctness (i.e., the soundness and completeness) of the transformation. In this paper we study how to avoid the risk, by imposing some conditions on programs in order to be safely unfolded. We show that these conditions are sufficient to guarantee the correctness of the unfolding transformation, and we present some experiments performed with the implemented online tool for illustrating the improved efficiency of unfolded programs.

Bousi[formula omitted]Prolog: Design and implementation of a proximity-based fuzzy logic programming language

The fuzzy logic programming language Bousi∼Prolog extends Prolog with a weak unification algorithm based on proximity relations and truth degree annotations. The weak unification algorithm makes the search for answers more flexible, while rule annotations make possible knowledge-based applications where the rules may be uncertain. In this paper, after recalling the main concepts supporting this language, we detail its design and implementation. We describe the implementation of its operational semantics, which is based on compiling programs and queries into Prolog, and those important features that makes it more applicable: fuzzy sets, integration with WordNet and efficiency techniques. The result is a high-level open-source implementation of the Bousi∼Prolog system, written on top of SWI-Prolog, and publicly available. We also summarise some experiments measuring its performance compared to other systems.

The Fuzzy Logic Programming language FASILL: Design and implementation

The FASILL programming language (acronym of “Fuzzy Aggregators and Similarity Into a Logic Language”) combines a weak unification algorithm, based on similarity relations, along with a rich repertoire of fuzzy connectives and aggregators, whose truth functions can be defined on a complete lattice. In this work, we want to provide a unified view of the fundamental concepts and ideas that inspired its construction, jointly with the implementation techniques that made it possible. We detail the implementation of the operational semantics and we describe the overall structure of the FASILL system. The FASILL language is well-suited for a wide range of applications and, specifically, it can help the development of knowledge based systems where to deal with uncertainty is important. After ten years designing and implementing fuzzy logic systems, this work culminates and agglutinates the experience acquired in our research group on the development of this kind of modern programming languages.

A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees

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.

Design and implementation of a fuzzy logic programming language using weak unification

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 fuzzy relations. Hence, it is a programming language well suited for dealing with vagueness and approximate reasoning. In this paper we summarize its design and implementation.

Categorical Description of Dynamic Fuzzy Logic Programming Language

First, this paper gives the relative basic theory of dynamic fuzzy logic, and introduces the basic data types of dynamic fuzzy logic. Then we introduce the basic data types of dynamic fuzzy logic and the abstract syntax of dynamic fuzzy logic programming language. At last, this paper gives the categorical description of dynamic fuzzy logic programming language according to categorical theory, and some examples are also given to demonstrate the theory put forward in the paper. The achievement of this paper will lay the foundation for the further development of dynamic fuzzy logic programming language.