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This contribution presents a user profile modelling approach based on fuzzy logic techniques. The proposed approach is conceived to find application in various contexts, with the aim of providing personalised contents to different categories of users. Both contents and users are described by metadata, so a description language is introduced along with a formal model defining their association mechanism. The strength of the model is the use of the expressive flexibility of fuzzy sets exploited by an innovative scheme of metadata. Along with the formal presentation of the profile modelling approach, the design of a software system based on a Service Oriented Architecture is presented. The system exposes a number of services to be consumed by information systems for personalized content access. In this way the system can be used in different application contexts. [ABSTRACT FROM AUTHOR]

Human-Centric Information Processing requires tight communication processes between users and computers. These two actors, however, traditionally use different paradigms for representing and manipulating information. Users are more inclined in managing perceptual information, usually expressed in natural language, whilst computers are formidable number-crunching systems, capable of manipulating information expressed in precise form. Fuzzy information granules could be used as a common interface for communicating information and knowledge, because of their ability of representing perceptual information in a computer manageable form. Nonetheless, this connection could be established only if information granules are interpretable, i.e. they are semantically co-intensive with human knowledge. Interpretable information granulation opens several methodological issues, regarding the representation and manipulation of information granules, the interpretability constraints and the granulation processes. By taking into account all such issues, effective Information Processing systems could be designed with a strong Human-Centric imprint. [ABSTRACT FROM AUTHOR]

Interpretability is one of the most important driving forces for the adoption of fuzzy rule-based classifiers. However, it is not given for granted, especially when fuzzy models are acquired from data. Therefore, evaluation of interpretability should be regarded as a major research topic. In this paper, we describe a technique for automatic interpretability assessment, based on the co-intension of the semantics of the knowledge base with the intrinsic semantics designated by linguistic labels. The core of the evaluation technique relies on the propositional view of rules and on logical operations. An illustrative example shows how the proposed approach can be useful in detecting lacks of interpretability for a simple knowledge base. [ABSTRACT FROM AUTHOR]

This paper presents a user profile modelling approach based on fuzzy logic techniques. The proposed approach is conceived to find application in the context of e-learning processes, with the aim of providing personalised contents to different categories of users. Several concepts are introduced and formalised within a peculiar mathematical framework which bases its working engine on an innovative scheme of metadata exploiting the expressive strength of fuzzy sets. Along with the formal presentation of the profile modelling approach, suitable examples of application are provided for the sake of illustration. [ABSTRACT FROM AUTHOR]

DC* (Double Clustering by A*) is an algorithm for interpretable fuzzy information granulation of data. It is mainly based on two clustering steps. The first step applies the LVQ1 algorithm to find a suitable representation of data relationships. The second clustering step is based on the A* search strategy and is aimed at finding an optimal number of fuzzy granules that can be labeled with linguistic terms. As a result, DC* is able to linguistically describe hidden relationships among available data. In this paper we propose an extension of the DC* algorithm, called DC$^{*} _{1.1}$, which improves the generalization ability of the original DC* by modifying the A* search procedure. This variation, inspired by Support Vector Machines, results empirically effective as reported in experimental results. [ABSTRACT FROM AUTHOR]

In this paper, we address the issue of designing optimal fuzzy interfaces, which are fundamental components of fuzzy models. In particular, we prove that for a class of so-called bi-monotonic fuzzy sets, optimal interfaces can be guaranteed if mild conditions hold. The class of bi-monotonic fuzzy sets covers a broad range of fuzzy sets shapes, including convex fuzzy sets, so that the provided theoretical results can be applied to a wide range of fuzzy models. As a practical result, the paper provides a set of guidelines that can be followed for the design of optimal fuzzy interfaces. [ABSTRACT FROM AUTHOR]