Resultados de búsqueda para Fuzzy Logic

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.

This paper presents a system implementing a novel addition to a fuzzy deductive database: hypothetical queries. Such queries allow users to dynamically make assumptions on a given database instance, either by adding or removing data, without changing the instance. Further, since a fuzzy database includes fuzzy relations, these relations can also be changed with assumptions. This ability for dynamic change seamlessly enables writing “what-if” applications such as decision-support systems. Here, the new language Fuzzy Hypothetical Datalog is presented, along with an operational semantics and stratified inference. It has been implemented in a working system DES readily available on-line.IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2020, pp. 1-8, doi: 10.1109/FUZZ48607.2020.9177715.

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/

Key Performance Indicators (KPIs) can be used to evaluate the success of an organization, facilitating the detection of the deviations and unexpected evolution of the behaviour of a company. The difficulty for enterprises is to ascertain what to do when a deviation is detected. In this paper, we propose a modelling approach to improve the operational business-level and to ascertain the possible actions that can be executed to maintain the right direction in a company. For business process-oriented companies, it entails knowing how KPIs can be affected by the business processes. It implies not only pointing out that a system malfunction exists, but also to know what to do when a deviation is detected. Our proposal presents a methodology that covers: (1) an extension of the existing models in order to combine KPIs, goals of the companies, and the decision variables together with business processes; (2) a methodology based on data mining analysis to verify the correctness of the enriched proposed model according to the data stored during business evolution, and; (3) a framework to simulate the evolution of the business according to the decisions taken in the governance process, thereby supporting governance activities to achieve the defined objectives by exploiting goals and KPIs from the proposed model.

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.