International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems

Ontologies have succeeded as a knowledge representation formalism in many domains of application. Nevertheless, they are not suitable to represent vague or imprecise information. To overcome this limitation, several extensions to classical ontologies based on fuzzy logic have been proposed. Even though different fuzzy logics lead to fuzzy ontologies with very different logical properties, the combined use of different fuzzy logics has received little attention to date. This paper proposes a fuzzy extension of the Description Logic — the logic behind the ontology language OWL 2 — that joins Gödel and Zadeh fuzzy logics. We analyze the properties of the new fuzzy Description Logic in order to provide guidelines to ontology developers to exploit the best features of each fuzzy logic. The proposal also considers degrees of truth belonging to a finite set of linguistic terms rather than numerical values, thus being closer to real experts' reasonings. We prove the decidability of the combined logic by presenting a reasoning preserving procedure to obtain a crisp representation for it. This result is generalized to offer a similar reduction that can be applied when any other finite t-norms, t-conorms, negations or implications are considered in the logic.

Risk analysis models describing aleatory (i.e., random) events contain parameters (e.g., probabilities, failure rates, …) that are epistemically-uncertain, i.e., known with poor precision. Whereas aleatory uncertainty is always described by probability distributions, epistemic uncertainty may be represented in different ways (e.g., probabilistic or possibilistic), depending on the information and data available.

The work presented in this paper addresses the issue of accounting for (in)dependence relationships between epistemically-uncertain parameters. When a probabilistic representation of epistemic uncertainty is considered, uncertainty propagation is carried out by a two-dimensional (or double) Monte Carlo (MC) simulation approach; instead, when possibility distributions are used, two approaches are undertaken: the hybrid MC and Fuzzy Interval Analysis (FIA) method and the MC-based Dempster-Shafer (DS) approach employing Independent Random Sets (IRSs). The objectives are: i) studying the effects of (in)dependence between the epistemically-uncertain parameters of the aleatory probability distributions (when a probabilistic/possibilistic representation of epistemic uncertainty is adopted) and ii) studying the effect of the probabilistic/possibilistic representation of epistemic uncertainty (when the state of dependence between the epistemic parameters is defined).

The Dependency Bound Convolution (DBC) approach is then undertaken within a hierarchical setting of hybrid (probabilistic and possibilistic) uncertainty propagation, in order to account for all kinds of (possibly unknown) dependences between the random variables.

The analyses are carried out with reference to two toy examples, built in such a way to allow performing a fair quantitative comparison between the methods, and evaluating their rationale and appropriateness in relation to risk analysis.

In this paper, we first defined intuitionistic normal fuzzy numbers as well as their operational laws and score function. Next, we proposed some aggregation operators including ordered intuitionistic normal ordered fuzzy weighted averaging operator, intuitionistic normal fuzzy ordered weighted geometric averaging operator, intuitionistic normal fuzzy related ordered weighted averaging operator, intuitionistic normal fuzzy related ordered weighted geometric averaging operator, induced intuitionistic normal fuzzy related ordered weighted averaging operator and induced intuitionistic normal fuzzy related ordered weighted geometric averaging operator. After that, similarity measure between two intuitionistic normal fuzzy numbers is defined. For multi-criteria decision making problems, in which the criteria are interactive and the criteria values are intuitionistic normal fuzzy numbers, an approach based on induced intuitionistic normal fuzzy related aggregation operators is proposed. And the comprehensive evaluation values of all alternatives can be derived by applying induced intuitionistic normal fuzzy related aggregation operators. Finally, the ranking of the whole alternatives set can be obtained by comparing the relative closeness of alternatives to the ideal solution. In the end, an example is given to show the validity and the feasibility of the method.

Despite the existence of different methods, including data mining techniques, available to be used in recommender systems, such systems still contain numerous limitations. They are in a constant need for personalization in order to make effective suggestions and to provide valuable information of items available. A way to reach such personalization is by means of an alternative data mining technique called classification based on association, which uses association rules in a prediction perspective. In this work we propose a hybrid methodology for recommender systems, which uses collaborative filtering and content-based approaches in a joint method taking advantage from the strengths of both approaches. Moreover, we also employ fuzzy logic to enhance recommendations' quality and effectiveness. In order to analyze the behavior of the techniques used in our methodology, we accomplished a case study using real data gathered from two recommender systems. Results revealed that such techniques can be applied effectively in recommender systems, minimizing the effects typical drawbacks they present.

In this paper, we provide alternative axioms and related axiomatizations of the Shapley value under interval uncertainty.