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El autor Antonio Becerra-Teron ha publicado 6 artículo(s):

1 - Type Checking and Testing of SPARQL Queries (Trabajo en progreso)

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.

Autores: Jesús M. Almendros-Jiménez / Antonio Becerra-Terón / 
Palabras Clave: Semantic Web - SPARQL - Testing - Type Systems

2 - FSA-SPARQL: Fuzzy Queries in SPARQL (Trabajo en progreso)

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

3 - Tuning Fuzzy SPARQL Queries in a Fuzzy Logic Programming Environment

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):

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

4 - Ontology and Constraint Reasoning Based Analysis of SPARQL Queries

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.

Autores: Jesus M. Almendros-Jimenez / Antonio Becerra-Teron / 
Palabras Clave: Databases - Debugging - Program analysis - SPARQL

5 - Declarative Debugging of SPARQL Queries

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 debuggerfor 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.

Autores: Jesus M. Almendros-Jimenez / Antonio Becerra-Teron / 
Palabras Clave: Debugging - Semantic Web - SPARQL - Tools

6 - Flexible Aggregation in FSA-SPARQL

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

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