Autor: Almendros-Jimenez, Jesus M.
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jalmen@ual.es
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Almendros-Jimenez
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Jesus M.
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Almendros Jiménez, Jesús M.
Almendros-Jiménez, Jesús M.
Almendros-Jiménez, Jesus M.
Almendros-Jiménez, Jesús M.
Almendros-Jiménez, Jesus M.
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Dept. of Informatics. University of Almería, Spain
Universidad de Almeria, Spain
Universidad de Almería
Dpto. de Informatica. Universidad de Almería. 04120-Almería. SPAIN.
Dept. of Informatics Universidad de Almería 04120 Almería (Spain)
Universidad de Almeria, Spain
Universidad de Almería
Dpto. de Informatica. Universidad de Almería. 04120-Almería. SPAIN.
Dept. of Informatics Universidad de Almería 04120 Almería (Spain)
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Artículo Fuzzy Retrieval of Linked Open DataAlmendros-Jimenez, Jesus M.; Becerra-Teron, Antonio; Moreno, Gines. Actas de las XXII Jornadas sobre Programación y Lenguajes (PROLE 2023), 2023-09-12.The technologies associated with the Semantic Web facilitate the publication of Open Data as well as their integration into the Linked Open Data cloud for which RDF format has been chosen. The promoted W3C RDF query language SPARQL became very popular and has been subject of study in recent years, with the goal of improving SPARQL engines in terms of functionality and usability. In this line of research, our research group has developed a fuzzy extension of SPARQL, called FSA-SPARQL (Fuzzy Sets and Aggregators based SPARQL). Here we go a step forward, adapting FSA-SPARQL for querying Linked Open Data with a fuzzy taste. Three goals are pursued, (i) fuzzification of Linked Open Data datasets, (ii) to extend FSA-SPARQL fuzzy aggregation and (iii) to equip FSA-SPARQL with quantification. Fuzzification, that is, interpretation of Linked Open Data datasets as fuzzy sets is made automatically at querying time, using trapezoidal membership functions. Powerful fuzzy aggregation and quantification operators are introduced in FSA-SPARQL based on fuzzy cardinals of fuzzy sets. As a result of the goals, FSA-SPARQL can be used for the retrieval of Linked Open Data datasets in real time, combining fuzzy aggregation and quantification. The proposed extension for FSA-SPARQL has been implemented and the system is available at our Web site.Artículo Declarative Debugging of SPARQL QueriesAlmendros-Jimenez, Jesus M.; Becerra-Teron, Antonio. Actas de las XX Jornadas de Programación y Lenguajes (PROLE 2021), 2021-09-22.The debugging of database queries is a research topic of increasing interest in recent years. The Semantic Web query language SPARQL should be equipped with a debugger for helping users to detect bugs which usually cause empty results as well as wrong and missing answers. Declarative debugging is a well-known debugging method successfully used in other database query languages. In this paper we present a declarative debugger for SPARQL. The debugging is based on the building of a debugging tree, and the detection of buggy and failure nodes in the debugging tree causing empty results as well as wrong and missing answers. The debugger has been implemented and it is available as Web tool.Artículo Testing of ATL programs from Randomly Generated Ecore Test ModelsAlmendros-Jimenez, Jesus M.; Becerra-Teron, Antonio. Actas de las XVI Jornadas de Programación y Lenguajes (PROLE 2016), 2016-09-02.Model transformation testing is crucial to detect incorrect transformations. Buggy transformations can lead to incorrect target models, either violating target meta-model requirements or more complex target model properties. In this paper we present a tool for testing ATL transformations. This tool is an extension of a previously developed tool for testing XML-based languages. With this aim an Ecore to XML Schema transformation is defined which makes to automatically generate random Ecore models possible. These randomly generated Ecore models are used to test ATL transformations. Properties to be tested are specified by OCL constraints, describing input and output conditions on source and target models, respectively.Artículo Fuzzy Queries of Social Networks involving Sentiment Analysis and Topic DetectionAlmendros-Jimenez, Jesus M.; Becerra-Teron, Antonio; Moreno, Gines. Actas de las XVIII Jornadas de Programación y Lenguajes (PROLE 2018), 2018-09-17.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.Artículo Type Checking and Testing of SPARQL QueriesAlmendros-Jimenez, Jesus M.; Becerra-Teron, Antonio. Actas de las XVII Jornadas de Programación y Lenguajes (PROLE 2017), 2017-07-19.In this paper we describe a property-based testing tool for SPARQL. The tool randomly generates test cases in the form of instances of an ontology. The tool checks the well typed-ness of the SPARQL query as well as the consistency of the test cases with the ontology axioms. With this aim, a type system has been defined for SPARQL. Test cases are after used to execute queries. The output of the queries are tested with a Boolean property which is defined in terms of membership of ontology individuals to classes. The testing tool reports counterexamples when the Boolean property is not satisfied.Artículo Ontology and Constraint Reasoning Based Analysis of SPARQL QueriesAlmendros-Jimenez, Jesus M.; Becerra-Teron, Antonio. Actas de las XIX Jornadas de Programación y Lenguajes (PROLE 2019), 2019-09-02.The discovery and diagnosis of wrong queries in database query languages have gained more attention in recent years. While for imperative languages well-known and mature debugging tools exist, the case of database query languages has traditionally attracted less attention. SPARQL is a database query language proposed for the retrieval of information in Semantic Web resources. RDF and OWL are standardized formats for representing Semantic Web information, and SPARQL acts on RDF/OWL resources allowing to retrieve answers of user’s queries. In spite of the SPARQL apparent simplicity, the number of mistakes a user can make in queries can be high and their detection, localization, and correction can be difficult to carry out. Wrong queries have as consequence most of the times empty answers, but also wrong and missing (expected but not found) answers. In this paper we present two ontology and constraint reasoning based methods for the discovery and diagnosis of wrong queries in SPARQL. The first method is used for detecting wrongly typed and inconsistent queries. The second method is used for detecting mismatching between user intention and queries, reporting incomplete, faulty queries as well as counterexamples. We formally define the above concepts and a batch of examples to illustrate the methods is shown.Artículo Introducing Fuzzy Quantifiers in FSA-SPARQLAlmendros-Jimenez, Jesus M.; Becerra-Teron, Antonio; Moreno, Gines. Actas de las XXI Jornadas de Programación y Lenguajes (PROLE 2022), 2022-09-05.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.Artículo FSA-SPARQL: Fuzzy Queries in SPARQLAlmendros-Jimenez, Jesus M.; Becerra-Teron, Antonio; Moreno, Gines. Actas de las XVII Jornadas de Programación y Lenguajes (PROLE 2017), 2017-07-19.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.Artículo Property based Testing of XQuery ProgramsAlmendros-Jimenez, Jesus M.; Becerra-Teron, Antonio. Actas de las XV Jornadas de Programación y Lenguajes (PROLE 2015), 2015-09-15.In this paper we present the elements of an XQuery testing tool which makes possible to automatically test XQuery programs. The tool is able to systematically generate XML instances (i.e., test cases) from a given XML schema. The number and type of instances is defined by the human tester. These instances are used to execute the given XQuery program. In addition, the tool makes possible to provide an user defined property to be tested against the output of the XQuery program. The property can be specified with a Boolean XQuery function. The tool is implemented as an oracle able to report whether the XQuery program passes the test, that is, all the test cases satisfy the property, as well as the number of test cases used for testing. In the case when the XQuery program fails the testing, the tool shows counterexamples found in the test cases. The tool has been implemented as an XQuery library which makes possible to be used from any XQuery interpreter.Artículo Binary Classification from Interval Constraint LearningAlmendros-Jimenez, Jesus M.; Becerra-Teron, Antonio. Actas de las XXIII Jornadas de Programación y Lenguajes (PROLE 2024), 2024-06-17.In this article, we present a novel method for binary classification based on interval constraint solving. A binary classification problem in our framework is formulated as a constraint-solving problem, where constraints are linear equations and membership to intervals. The framework adopts fuzzy logic with two goals. The first is that the datasets are mapped to fuzzy sets in such a way that the data coordinates are rescaled to the interval [0,1]. Additionally, fuzzy set mapping enables redistributing data in the fuzzy space. A constraint solver CLP(BNR) is used to solve the constraints, which acts as a learning method. A classifier in our framework is a solution to the constraints problem consisting of a set of interval constraints expressing the bounds for the coefficients of linear equations and a set of fuzzy sets. The underlined classification procedure requires checking the satisfiability of the interval constraints from the mapping of data into fuzzy sets. An ensemble of the learning method is also proposed to improve classification.