El autor Gines Moreno ha publicado 16 artículo(s):

Wide datasets are usually used for training and validating neural networks, which can be later tuned in order to correct their behaviors according to a few number of test cases proposed by users. In this paper we show how the FLOPER system developed in our research group is able to perform this last task after coding a neural network with a fuzzy logic language where program rules extend the classical notion of clause by including on their bodies both fuzzy connectives (useful for modeling activation functions of neurons) and truth degrees (associated to weights and bias in neural networks). We present an online tool which helps to select such operators and values in an automatic way, accomplishing with our recent technique for tuning this kind of fuzzy programs. Moreover, we provide some experimental results revealing that our tool generates the choices that better fit user’s preferences in a very efficient way, and producing relevant improvements on tuned neural networks.

Autores: Gines Moreno / Jesús Pérez / José Antonio Riaza Valverde / 
Palabras Clave: Fuzzy Logic Programming - Neural Networks - tuning

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.Paper accepted in IEEE International Conf. on Fuzzy Systems 2021 https://attend.ieee.org/fuzzieee-2021/

Autores: Jesus M. Almendros-Jimenez / Antonio Becerra-Teron / Gines Moreno / José Antonio Riaza Valverde / 
Palabras Clave: Fuzzy Logic - Semantic Web - SPARQL

We have recently designed/implemented a method for debugging Fuzzy-XPath queries which produces a set of alternative Fuzzy-XPath expressions with higher chances for retrieving answers from XML files. The main goal of the present paper consists in the introduction of a new fuzzy command inside the Fuzzy-XPath debugger which comfortably relies on our implementation based on fuzzy logic programming. So, when <<[FILTER=r]>> precedes a fuzzy query the debugger lazily explores an input XML document for dynamically disregarding as soon as possible those branches of the XML tree leading to irrelevant solutions (i.e., with a chance degree degraded below r), thus allowing the possibility of efficiently managing large files without reducing the set of answers for which users are mainly interested in. Hence, advice that this dynamic thresholding technique embedded into the core of the Fuzzy-XPath debugger has two advantages: • firstly it permits to concentrate on significant answers (i.e., alternative queries which do not excessively deviate from the original one) without disturbing the attention with useless information, and • secondly, the computational behavior of the debugging process is highly improved (both in time and space) since a great amount of work is avoided when discriminating useless branches of the XML tree.

Autores: Jesus M. Almendros-Jiménez / Alejandro Luna / Ginés Moreno / 
Palabras Clave:

Cloud computing enables elasticity – rapid provisioning and deprovisioning of computational resources. Elasticity allows cloud users to quickly adapt resource allocation to meet changes in their workloads. For cloud providers, elasticity complicates capacity management as the amount of resources that can be requested by users is unknown and can vary significantly over time. Overbooking techniques allow providers to increase utilization of their data centers. For safe overbooking, cloud providers need admission control mechanisms to handle the tradeoff between increased utilization (and revenue), and risk of exhausting resources, potentially resulting in penalty fees and/or lost customers. We propose a flexible approach (implemented with fuzzy logic programming) to admission control and the associated risk estimation. Our measures exploit different fuzzy logic operators in order to model optimistic, realistic, and pessimistic behaviour under uncertainty. An experimental evaluation confirm that our fuzzy admission control approach can significantly increase resource utilization while minimizing the risk of exceeding the total available capacity.

Autores: Carlos Vázquez / Ginés Moreno / Luis Tomás / Johan Tordsson / 
Palabras Clave: Admission Control - Cloud Computing - Fuzzy Logic Programming - Risk Assessment

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 not dealing with similarity relations, in this work we try to adapt such operation to the so-called FASILL language (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) unifers at unfolding time could destroy the correctness of the transformation. In this paper we study how to avoid this risk by compiling what we call similarity constraints on transformed rules, whose further evaluation is delayed at running time. Moreover, our technique minimizes the size and number of occurrences of such constructs in transformed programs to gain efficiency while preserving semantics.

Autores: Pascual Julian-Iranzo / Gines Moreno / José Antonio Riaza Valverde / 
Palabras Clave: Fuzzy Logic Programming - Similarity Relations - Unfolding

n this paper we report a preliminary work about XPath debugging. We will describe how we can manipulate an XPath expression in order to obtain a set of alternative XPath expressions that match to a given XML document. For each alternative XPath expression we will give a chance degree that represents the degree in which the expression deviates from the initial expression. Thus, our work is focused on providing the programmer a repertoire of paths that (s)he can use to retrieve answers. The approach has been implemented and tested.

Autores: Jesús M. Almendros-Jiménez / Alejandro Luna / Ginés Moreno / 
Palabras Clave: Debugging - Fuzzy (Multi-adjoint) Logic Programming - XPath

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 FASILL, 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 FASILL and (2) apply the tuning techniques to FSA-SPARQL queries for getting more precise formulation of queries from test cases.Artículo pendiente de publicación en el 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2019): http://sites.ieee.org/fuzzieee-2019/

Autores: Jesus M. Almendros-Jimenez / Antonio Becerra-Teron / Gines Moreno / Jose Antonio Riaza Valverde / 
Palabras Clave: Databases - Fuzzy - Logic Programming - SPARQL

Fuzzy logic programming is a growing declarative paradigm aiming to integrate fuzzy logic into logic programming. One of the most difficult tasks when specifying a fuzzy logic program is determining the right weights for each rule, as well as the most appropriate fuzzy connectives and operators. In this paper, we introduce a symbolic extension of fuzzy logic programs in which some of these parameters can be left unknown, so that the user can easily see the impact of their possible values. Furthermore, given a number of test cases, the most appropriate values for these parameters can be automatically computed.

Autores: Ginés Moreno / Jaime Penabad / Germán Vidal / 
Palabras Clave: Fuzzy Logic Programming - symbolic execution - tuning

Social networks have become a source of data which are of interest in all areas, and their querying and analysis is a hot topic in computer science. Our research group has developed a fuzzy extension of the Semantic Web query language SPARQL, called FSA-SPARQL (Fuzzy Sets and Aggregators based SPARQL). This extension provides mechanisms to express fuzzy queries against RDF data. FSA-SPARQL works with social networks. With this aim, FSA-SPARQL enables the transformation and fuzzification of social network API data. Fuzzification of social networks data is automatic and user-defined enabling a wide range of mechanisms for ranking and categorization, including sentiment analysis and topic detection. As case study, FSA-SPARQL has been used to query three well-known social networks: Twitter, Foursquare and TMDb.

Autores: Jesús M. Almendros Jiménez / Antonio Becerra Terón / Ginés Moreno / 
Palabras Clave: Fuzzy Logic - Semantic Web - Social Networks - SPARQL

Classically, most programming languages use in a predefined way the notion of «string» as an standard data structure for a comfortable management of arbitrary sequences of characters. However, in this paper we assign a different role to this concept: here we are concerned with fuzzy logic programming, a somehow recent paradigm trying to introduce fuzzy logic into logic programming. In this setting, the mathematical concept of multi-adjoint lattice has been successfully exploited into the so-called Multi-adjoint Logic Programming approach, MALP in brief, for modeling flexible notions of truth-degrees beyond the simpler case of true and false. Our main goal points out not only our formal proof verifying that stringbased lattices accomplish with the so-called multi-adjoint property (as well as its Cartesian product with similar structures), but also its correspondence with interesting debugging tasks into the FLOPER system (from «Fuzzy LOgic Programming Environment for Research») developed in our research group.

Autores: Pedro J. Morcillo / Ginés Moreno / Jaime Penabad / Carlos Vázquez / 
Palabras Clave: Cartesian Product of Multi-adjoint Lattices - Declarative Debugging - Fuzzy (Multi-adjoint) Logic Programming

In many declarative frameworks, unfolding is a very well-known semantics-preserving transformation technique based on the application of computational steps on the bodies of program rules for improving efficiency. In this paper we describe an online tool which allows us to unfold a symbolic extension of a modern fuzzy logic language where program rules can embed concrete and/or symbolic fuzzy connectives and truth degrees on their bodies. The system offers a comfortable interaction with users for unfolding symbolic programs and it also provides useful options to navigate along the sequence of unfolded programs. Finally, the symbolic unfolding transformation is connected with some fuzzy tuning techniques that we previously implemented on the same tool.

Autores: Ginés Moreno / José Antonio Riaza Valverde / 
Palabras Clave: Fuzzy Logic Programming - Software Tools - symbolic execution - Unfolding

SPARQL has been adopted as query language for the Semantic Web. RDF and OWL have been also established as vocabularies to describe ontologies in this setting. While RDF/OWL/SPARQL have been designed for querying crisp information, some contexts require to manage uncertainty, vagueness and imprecise knowledge. In this paper we propose a SPARQL extension, called FSA-SPARQL (Fuzzy Sets and Aggregators based SPARQL) in which queries can involve different fuzzy connectives and (aggregation) operators. The language has been implemented as an extension of the ARQ Jena SPARQL engine and it is equipped with a Web tool from which queries can be executed on-line.

Autores: Jesús M. Almendros-Jiménez / Antonio Becerra-Terón / Ginés Moreno / 
Palabras Clave: Fuzzy Logic - Semantic Web - SPARQL

This work proposes a declarative semantics based on a fuzzy variant of the classical notion of least Herbrand model for the so-called FASILL language (acronym of “Fuzzy Aggregators and Similarity Into a Logic Language”) which has being recently designed and implemented in our research group for coping with implicit/explicit truth degree annotations, a great variety of connectives and unification by similarity.

Autores: Pascual Julián-Iranzo / Ginés Moreno / Jaime Penabad / Carlos Vázquez / 
Palabras Clave: Fuzzy Logic Programming - Herbrand Model - Similarity Relations

Fuzzy quantification makes it possible to model quantifiers from the natural language (most of, at least half, few, around a dozen, etc). Absolute quantifiers refer to a number while relative ones refer to a proportion. In this paper we introduce fuzzy quantifiers in FSA-SPARQL, a fuzzy extension of the SPARQL query language developed by our group.We focus on relative quantifiers (most of, at least half, few etc) and propose a fuzzy operator called QUANT to model relative fuzzy quantifiers in FSA-SPARQL. As in previous works about FSA-SPARQL, we study a translation of FSA-SPARQL queries involving fuzzy quantifiers to crisp SPARQL. The proposed extension has been implemented and it can be tested from the FSA-SPARQL Web site.

Autores: Jesus M. Almendros-Jimenez / Antonio Becerra-Teron / Gines Moreno / 
Palabras Clave: Database Query Languages - Fuzzy Logic - Semantic Web - SPARQL