Fuzzy Description Logics (DLs) are are a family of knowledge representation formalisms designed to represent and reason about vague and imprecise knowledge that is inherent to many application domains. Previous work has shown that the complexity of reasoning in a fuzzy DL using finitely many truth degrees is usually not higher than that of the underlying classical DL. We show that this does not...

Based on the fully implicational idea, we investigate the interval-valued fuzzy reasoning with multiantecedent rules. First, we construct a class of interval-valued fuzzy implications by means of a type of implications and a parameter on the unit interval, then use them to establish three kinds of fully implicational reasoning methods for the interval-valued fuzzy reasoning with multi-anteceden...

This paper proposes probabilistic default reasoning as a suitable approach to inheritance and recognition in uncertain and fuzzy object-oriented models. Firstly, we introduce an uncertain and fuzzy object-oriented model where a class property (i.e., an attribute or a method) can contain fuzzy sets interpreted as families of probability distributions, and uncertain class membership and property ...

The integration of distinct reasoning styles such as the ones exploited by description logics and rule-based systems is still an open challenge because of the differences among them. Such integration may be achieved by following two complementary approaches: loose integration vs. tight integration. Loosely integrated hybrid systems couple existing tools, so they have to handle mutual interactio...

Recommender System is an effective means of handling information overload and can provide personalized service as a useful information tool in e-commerce. In this paper, a novel automatic recommender system is proposed based on fuzzy c-means algorithm and rough set theory, including three main steps: data discretization, rules establishing and fuzzy reasoning. A method for fitting the results o...

Although the region connection calculus (RCC) offers an appealing framework for modelling topological relations, its application in real–world scenarios is hampered when spatial phenomena are affected by vagueness. To cope with this, we present a generalization of the RCC based on fuzzy set theory, and discuss how reasoning tasks such as satisfiability and entailment checking can be cast into l...

This paper presents the application of a modified fuzzy reasoning spiking neural P systems (MFRSN P system, for short) to fault diagnosis of metro traction power supply systems. In MFRSN P systems, three types of neurons are used to represent operation information of protection devices including protective relays and circuit breakers; a reasoning algorithm associated with MFRSN P systems is int...

In recent years there has been a growing interest in the combination of rules and ontologies. Notably, many works have focused on the theoretical aspects of such integration, sometimes leading to concrete solutions. However, solutions proposed so far typically reason upon crisp concepts, while concrete domains require also fuzzy expressiveness. In this work we combine mature technologies, namel...

Task assignment processes and its control implying reasoning about objects and resources and their changing states are dominated by discrete or stochastic-event dynamics or both. Estimating the components position of the mobile robot provided by sensor generates unknown, hidden variables which will be model by the means of probabilistic inference taking into account incomplete and uncertain inf...

Fuzzy relation equations are used to investigate theoretical and applicational aspects of fuzzy set theory, e.g., approximate reasoning, time series forecast, decision making and fuzzy control, etc.. This paper relates these equations to a particular kind of concept lattices.