Decision making with a fuzzy ontology

Abstract

Knowledge mobilisation is a transition from the prevailing knowledge management technology that has been widely used in industry for the last 20 years to a new methodology and some innovative methods for knowledge representation, formation and development and for knowledge retrieval and distribution. Knowledge mobilisation aims at coming to terms with some of the problems of knowledge management and at the same time to introduce new theory, new methods and new technology. More precisely, this paper presents an outline of a fuzzy ontology as an enhanced version of classical ontology and demonstrates some advantages for practical decision making. We show that a number of soft computing techniques, e.g. aggregation functions and interval valued fuzzy numbers, will support effective and practical decision making on the basis of the fuzzy ontology. We demonstrate the knowledge mobilisation methods with the construction of a support system for finding the best available wine for a number of wine drinking occasions using a fuzzy wine ontology and fuzzy reasoning methods; the support system has been implemented for a Nokia N900 smart phone.

A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis

“…Fuzzy ontology can come from two sources: mapping of a fuzzy database [42] or as an extension of crisp ontology [40]. Fuzzy ontology has been used in medical and non-medical systems [41,[43][44][45][46][47]. Ali et al [47] proposed T2FOBOMIE; this system is an opinion mining system based on a type-2 fuzzy rough ontology.…”

“…Torshizi et al [43] utilized fuzzy ontology to build an intelligent rule-based system to determine the severity of Benign Prostatic Hyperplasia and recommend the appropriate therapies. Carlsson et al [44] discussed the capabilities of fuzzy ontology over crisp one and utilized it in a knowledge mobilization application. Mezei et al [45] asserted that fuzzy ontology is critical to building actionable knowledge to aid complex decisions, and they proposed a fuzzy wine ontology.…”

Decision analytics mobilized with digital coaching

“…In an industrial corporate context, the managers and experienced engineers in charge of supply chains, production, distribution and logistics, the marketing managers in charge of customers and markets, the financial managers and finance professionals in charge of corporate finance operations, and the information and communication professionals and managers in charge of human resources and so on all need and are assumed to sufficiently master the analytics tools they need for optimal performance. We have found out in work with industrial partners (Carlsson, ; Carlsson, Brunelli, & Mezei, ; Carlsson et al, ; Carlsson, Mezei, & Brunelli, ; Carlsson, Mezei, & Brunelli, )—if we for a moment switch to first‐hand experience—that in a typical industrial plant there are three levels of knowledge and skills: (i) some managers have some knowledge of the key concepts and an intuitive understanding of what can be done with analytics; (ii) some managers have sufficient insight and knowledge to read (written reports or reports produced with interactive information systems (IS)), absorb and make decisions based on analytics results; (iii) finally, some managers have the skills and knowledge to work with analytics tools and make use of analysis results. In a digital economy, the level (ii) is a minimum requirement, but before long the level (iii) capabilities will be needed (Davenport & Harris, ).…”

Building and managing fuzzy ontologies with heterogeneous linguistic information

“…In such a way, user-computer communication is improved since 38 users feel more comfortable when using the system. 39 Ó 2015 Published by Elsevier B.V. 40 41 42 43 1. Introduction 44 Ontologies have become an important tool in the domain mod- 45 eling field.…”

“…In order 151 to solve this kind of situations, it is mandatory that users are 152 allowed to work with LTSs that are specifically designed for them. 153 In order to solve this problem, multi-granular When information is stored in an FO, it is possible to formulate 260 queries in order to retrieve some required information [41]. Let multi-granular information in the ontology creation process are 322 exposed.…”