On the similarity relation within fuzzy ontology components

Ontology reuse is an important research issue. Ontology merging, integration, mapping, alignment and versioning are some of its subprocesses. A considerable research work has been conducted on them. One common issue to these subprocesses is the problem of defining similarity relations among ontologies components. Crisp ontologies become less suitable in all domains in which the concepts to be represented have vague, uncertain and imprecise definitions. Fuzzy ontologies are developed to cope with these aspects. They are equally concerned with the problem of ontology reuse. Defining similarity relations within fuzzy context may be realized basing on the linguistic similarity among ontologies components or may be deduced from their intentional definitions. The latter approach needs to be dealt with differently in crisp and fuzzy ontologies. This is the scope of this paper.ou

Derivation of diagnostic models based on formalized process knowledge

© IFAC.Industrial systems are vulnerable to faults. Early and accurate detection and diagnosis in production systems can minimize down-time, increase the safety of the plant operation, and reduce manufacturing costs. Knowledge- and model-based approaches to automated fault detection and diagnosis have been demonstrated to be suitable for fault cause analysis within a broad range of industrial processes and research case studies. However, the implementation of these methods demands a complex and error-prone development phase, especially due to the extensive efforts required during the derivation of models and their respective validation. In an effort to reduce such modeling complexity, this paper presents a structured causal modeling approach to supporting the derivation of diagnostic models based on formalized process knowledge. The method described herein exploits the Formalized Process Description Guideline VDI/VDE 3682 to establish causal relations among key-process variables, develops an extension of the Signed Digraph model combined with the use of fuzzy set theory to allow more accurate causality descriptions, and proposes a representation of the resulting diagnostic model in CAEX/AutomationML targeting dynamic data access, portability, and seamless information exchange

Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis

In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments

A Formal Approach based on Fuzzy Logic for the Specification of Component-Based Interactive Systems

Formal methods are widely recognized as a powerful engineering method for the specification, simulation, development, and verification of distributed interactive systems. However, most formal methods rely on a two-valued logic, and are therefore limited to the axioms of that logic: a specification is valid or invalid, component behavior is realizable or not, safety properties hold or are violated, systems are available or unavailable. Especially when the problem domain entails uncertainty, impreciseness, and vagueness, the appliance of such methods becomes a challenging task. In order to overcome the limitations resulting from the strict modus operandi of formal methods, the main objective of this work is to relax the boolean notion of formal specifications by using fuzzy logic. The present approach is based on Focus theory, a model-based and strictly formal method for componentbased interactive systems. The contribution of this work is twofold: i) we introduce a specification technique based on fuzzy logic which can be used on top of Focus to develop formal specifications in a qualitative fashion; ii) we partially extend Focus theory to a fuzzy one which allows the specification of fuzzy components and fuzzy interactions. While the former provides a methodology for approximating I/O behaviors under imprecision, the latter enables to capture a more quantitative view of specification properties such as realizability.Comment: In Proceedings FESCA 2015, arXiv:1503.0437