Semantic Data Management Using Fuzzy Relational Databases

Campaña, Jesús R.; Medina, Juan Miguel; Vila, M. A.

doi:10.1007/978-3-319-00954-4_6

Cited by 3 publications

References 37 publications

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Fuzzy Recursive Relationships in Relational Databases

Myszkorowski

2018

Information System in Management

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Recursive relationships are used for modelling problems coming from the real life, such as, for example, a relationship describing formal dependencies between employees of an enterprise, where creation of work groups and teams requires analysis of many elements. In conventional database systems, the precision of data is assumed. If our knowledge of the fragment of reality to be modelled is imperfect one should apply tools for describing uncertain or imprecise information. One of them is the fuzzy set theory. The paper deals with recursive relationships in fuzzy databases. The analysis is performed with the use of the theory of interval-valued fuzzy sets. A definition of a fuzzy interval recursive relationship has been presented. The paper defines different connections of entities which participate in such relationships. Op-erations of the extended relational algebra are also discussed.

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Fuzzy Recursive Relationships in Relational Databases

Myszkorowski

2018

Information System in Management

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Fuzzy Logic-Based Inference Systems

2015

Fuzzy Logic-Based Modeling in Collaborative and Blended Learning

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This chapter presents the mathematical formulation of the fuzzy logic-based inference systems, used as means to infer about the response of ill-conditioned systems, based on the field knowledge representation in the fuzzy world. Particular approaches are explored, e.g., Fuzzy Inference System (FIS), Adaptive Networks-based FIS (ANFIS), Intuitionistic FIS (IFIS) and Fuzzy Cognitive Map (FCM), surfacing their potentialities in modeling applications, such as those in the field of learning, examined in the chapters of Part III that follow.

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A Framework for Fuzzy Ontology Storing onto Relational Database within an a Priori Data Integration System

Berber¹,

Beldjilali²,

Lamolle³

2015

IJCA

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Resolving semantic heterogeneity is still a challenging issue in data integration systems; but it can be strongly fixed when using ontology in an a priori approach where local ontology concepts are linked with shared ontology prior to populating data in corresponding sources. In this paper, we describe a defying context where local source is described by a fuzzy OWL ontology within an integration system using an a priori approach to achieve automatic integration for new data sources. We propose a conceptual framework starting by shared ontology and producing a target fuzzy Relational Database for every ontology-based local source participating in the integration system. Assuming shared ontology is a consensus in a given domain, this framework provides various contributions. It aims to solve ahead the problem of heterogeneous data sources because the local ontology that references the shared ontology is used to generate the conceptual data model for the target fuzzy Relational Database. To do this, it extends the a priori approach to deal with uncertainty which is a very common requirement in real world applications. Its storage process may be run on most of popular RDBMS. It is using a fuzzy OWL which represents most of fuzzy ontology constructs.

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Semantic Data Management Using Fuzzy Relational Databases

Cited by 3 publications

References 37 publications

Fuzzy Recursive Relationships in Relational Databases

Fuzzy Recursive Relationships in Relational Databases

Fuzzy Logic-Based Inference Systems

A Framework for Fuzzy Ontology Storing onto Relational Database within an a Priori Data Integration System

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