Aggregation procedure based on majority principle for collective identification of firm’s crucial knowledge

It is very important to identify, preserve, and transfer knowledge to those who need it within firm. However, the identification of knowledge and especially tacit knowledge is a complex process because knowledge cannot be measured quantitatively. In this paper we present an approach for inducing a set of collective decision rules representing a generalized description of the preferential information of a group of decision makers involved in a multicriteria classification problem to identify crucial knowledge to be capitalized

Challenges in Capitalizing Knowledge in Innovative Product Design Process.

Capitalizing on company’s knowledge is increasingly being recognized in a private organizations environment since managing knowledge productivity is considered a source of competitive advantage. In this paper we present a generalization of GAMETH framework, that play an important role in identifying crucial knowledge used and created in innovative product design process. Thus, we have developed a method based on three phases. In the first phase, we have used GAMETH to identify the set of “reference knowledge”. During the second phase, decision rules are inferred, through rough sets theory, from decision assignments provided by the decision maker(s). In the third phase, a multicriteria classification of “potential crucial knowledge” is performed on the basis of the decision rules that have been collectively identified by the decision maker(s).Dominance Rough set approach; Decision rules; Multi- criteria classification; crucial knowledge; Knowledge Capitalizing;

Initiating organizational memories using ontology-based network analysis as a bootstrapping tool

An important problem for many kinds of knowledge systems is their initial set-up. It is difficult to choose the right information to include in such systems, and the right information is also a prerequisite for maximizing the uptake and relevance. To tackle this problem, most developers adopt heavyweight solutions and rely on a faithful continuous interaction with users to create and improve content. In this paper, we explore the use of an automatic, lightweight ontology-based solution to the bootstrapping problem, in which domain-describing ontologies are analysed to uncover significant yet implicit relationships between instances. We illustrate the approach by using such an analysis to provide content automatically for the initial set-up of an organizational memory

Generalizing GAMETH: Inference rule procedure..

In this paper we present a generalisation of GAMETH framework, that play an important role in identifying crucial knowledge. Thus, we have developed a method based on three phases. In the first phase, we have used GAMETH to identify the set of “reference knowledge”. During the second phase, decision rules are inferred, through rough sets theory, from decision assignments provided by the decision maker(s). In the third phase, a multicriteria classification of “potential crucial knowledge” is performed on the basis of the decision rules that have been collectively identified by the decision maker(s).Knowledge Management; Knowledge Capitalizing; Managing knowledge; crucial knowledge;

Enterprise engineering using semantic technologies

Modern Enterprises are facing unprecedented challenges in every aspect of their businesses: from marketing research, invention of products, prototyping, production, sales to billing. Innovation is the key to enhancing enterprise performances and knowledge is the main driving force in creating innovation. The identification and effective management of valuable knowledge, however, remains an illusive topic. Knowledge management (KM) techniques, such as enterprise process modelling, have long been recognised for their value and practiced as part of normal business. There are plentiful of KM techniques. However, what is still lacking is a holistic KM approach that enables one to fully connect KM efforts with existing business knowledge and practices already in IT systems, such as organisational memories. To address this problem, we present an integrated three-dimensional KM approach that supports innovative semantics technologies. Its automated formal methods allow us to tap into modern business practices and capitalise on existing knowledge. It closes the knowledge management cycle with user feedback loops. Since we are making use of reliable existing knowledge and methods, new knowledge can be extracted with less effort comparing with another method where new information has to be created from scratch

Initiating organizational memories using ontology network analysis

One of the important problems in organizational memories is their initial set-up. It is difficult to choose the right information to include in an organizational memory, and the right information is also a prerequisite for maximizing the uptake and relevance of the memory content. To tackle this problem, most developers adopt heavy-weight solutions and rely on a faithful continuous interaction with users to create and improve its content. In this paper, we explore the use of an automatic, light-weight solution, drawn from the underlying ingredients of an organizational memory: ontologies. We have developed an ontology-based network analysis method which we applied to tackle the problem of identifying communities of practice in an organization. We use ontology-based network analysis as a means to provide content automatically for the initial set up of an organizational memory

Mémoire organisationnelle adaptative en vue d'une classification automatique pour la capitalisation d'informations

International audienceUne mémoire organisationnelle est un espace de capitalisation d’informations diverses circulant au sein d’une entreprise. L’objectif de nos travaux est d’assister les usagers dans le classement de ces informations au sein de cette mémoire organisationnelle tout en limitant leur contribution. Pour ce faire, nous proposons un méta-modèle de mémoire organisationnelle sur lequel nous souhaitons faire reposer un processus de classification automatique