Fuzzy functional dependencies with liguistic quantifiers.

Extending PSL with Fuzzy Quantifiers

Golnoosh Farnadi, Stephen H. Bach, Marie-Francine Moens, Lise Getoor, Martine De Cock Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium, Department of Computer Science, Katholieke Universiteit Leuven, Belgium, Statistical Relational Learning Group, University of Maryland, USA, University of California, Santa Cruz, USA, Center for Data Science, Univers...

Incremental Discovery of Fuzzy Functional Dependencies

Fuzzy Functional Dependencies in Relational Databases

The work on this paper is an application of fuzzy set theory in relational databases. The authors introduce a new notion of fuzzy functional dependencies (ffd) in relational databases. It is observed that the existing notions of fuzzy functional dependencies (ffd) have a common drawback. Consequently the need for introducing a new type of fuzzy functional dependencies (ffd) is justified, and ex...

Automated Inference with Fuzzy Functional Dependencies over Graded Data

Fuzzy set theory has proved to be a successful paradigm to extend the database relational model, augmenting its skill to capture uncertaintly. This capability may be consider in two levels: the data itself and the constraints defined to adjust the database schema to the real system. When constraints are considered, it is necessary to design methods to reason about it and not only a way to expre...

Discovering Fuzzy Functional Dependencies as Semantic Knowledge in Large Databases

Fuzzy functional dependency (FFD) is a kind of semantic knowledge and can be discovered from a large volume of business data. Sectional FFD and Attribute FFD are discussed so as to reflect semantics of the business world and express useful information that is natural for people to comprehend. The experimental results on an insurance data set show that the proposed method can extract knowledge e...