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 F A S I L L that offers powerful tuning capabilities that can accept applications in many fields. The purpose of this paper is to show how the F A S I L L 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 F A S I L L transformation, data fuzzification and query customization in FSA-SPARQL become F A S I L L tuning problems. We have validated the approach with queries against datasets from online communities.

Abstract Tau Prolog is a client-side Prolog interpreter fully implemented in JavaScript, which aims at implementing the ISO Prolog Standard. Tau Prolog has been developed to be used with either Node.js or a browser seamlessly, and therefore, it has been developed following a non-blocking, callback-based approach to avoid blocking web browsers. Taking the best from JavaScript and Prolog, Tau Prolog allows the programmer to handle browser events and manipulate the Document Object Model (DOM) of a web using Prolog predicates. In this paper we describe the architecture of Tau Prolog and its main packages for interacting with the Web, and we present its programming environment.

A substitution is a mapping from variables to terms. There is a consensus in the Logic Programming community that the work with substitutions is a source of errors due to their poor algebraic properties. This problem increases when we manipulate substitutions in the framework of similarity relations, where some classical concepts have more complex definitions and the intuition of the result of the operations involving them is easily lost. In this work we analyze some algebraic properties of substitutions in the framework of similarity relations. Specifically, we study the lattice structure of the set of idempotent substitutions with the definition of the weak parallel composition and weak parallel factorization operations, acting as the join and meet of the lattice. Also we relate these operations with the classical operation of composition of substitutions. The aim is to provide fuzzy logic programming researchers with reasoning tools with richer properties that facilitate formal proofs and, even, able to define compositional and parallel semantics.

Aggregation is a very useful operation in database query languages. Through count, sum, min, max and avg operators database instances can be counted and summarized. Attached to such operators, group by and having clauses make it possible to define partitions on database instances as well as filter partitions according to Boolean conditions. In this paper, we define aggregation operators for the language FSA-SPARQL, which is a fuzzy extension of the Semantic Web query language SPARQL. We present the semantics of such operators with regard to fuzzy RDF triple patterns. We also provide mechanisms in FSA-SPARQL for the partition of fuzzy RDF triple patterns with regard to fuzzy sets, as well as for the filtering of partitions. The proposed extension has been implemented and it can be tested from the FSA-SPARQL Web site.

During the last years we have developed advanced tools for tuning fuzzy logic programs devoted to facilitate the selection of the more appropriate set of weights and fuzzy connectives used in programs rules. Designing accurate techniques for automating these tasks is very useful for programmers, even when they are time consuming. In order to increase its performance, in this paper we make use of powerful and well-known SAT/SMT solvers for improving our original approaches. Inspired by some previous experiences we have acquired in this setting, whose impact is growing in many modern software tools, we show some representative experiments (related to circuit validation and linear regression) and benchmarks which illustrate the significant advantages enjoyed by the new empowered method.

We have recently designed FSA-SPARQL, an extension of the SPARQL query language for querying fuzzy RDF datasets. Answers of FSA-SPARQL queries are usually annotated with truth degrees which are computed from fuzzy connectives and operators that act on truth degrees associated to RDF triples. While FSA-SPARQL offers a rich repertoire of fuzzy connectives and operators, it is not always easy to retrieve the user's expected answers. This is very often due to wrong formulation of queries, caused by inadequate use/combination of fuzzy connectives, operators and thresholds. For instance, a high threshold for truth degrees in some RDF datasets can lead to an empty set of answers, some strong or weak restrictive combination of fuzzy conditions might produce few or too many answers, etc. On the other hand, our research group has also developed the fuzzy logic programming language $\mathcal{F}\mathcal{A}\mathcal{S}\mathcal{I}\mathcal{L}\mathcal{L}$, which has been equipped with tuning techniques for enabling the customization of queries from test cases. In this paper, our goals are: (1) to provide a FSA-SPARQL translation to $\mathcal{F}\mathcal{A}\mathcal{S}\mathcal{I}\mathcal{L}\mathcal{L}$ and (2) apply the tuning techniques to FSA-SPARQL queries for getting more precise formulation of queries from test cases.