Resultados de búsqueda para Semantic Web

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.

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.

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/

This paper describes a first effort for the semantic mediation between heterogeneous environmental observation datasets through the Sensor Observation Service (SOS) standard proposed by the Open Geospatial Consortium. The solution enables application domain experts to provide an ontology with semantic data integration knowledge, which is next combined with data source knowledge during the evaluation of global SOS GetObservation requests. This enables the development of a more general purpose solution that may be adapted to different application domains by just changing the ontology. Besides, users without specific application domain skills and knowledge may now develop new semantically enabled applications.Finally, the design of the framework is based on the well-known Mediator/Wrapper architecture and follows a Local As View data integration approach, which simplifies the incorporation of new datasets without having to change the existing data integration knowledge.

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.

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.

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.