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Methodology to design ontologies from organizational models: application to creativity workshops

Published online by Cambridge University Press:  06 May 2019

Gabriel Alex ,
Barrios Pedro Chavez  and
Monticolo Davy
Gabriel Alex
Affiliation:
CPI Laboratory, Arts et Métiers ParisTech, Paris, France
Barrios Pedro Chavez
Affiliation:
ERPI Laboratory, Université de Lorraine, Nancy, France
Monticolo Davy*
Affiliation:
ERPI Laboratory, Université de Lorraine, Nancy, France
*
Author for correspondence: Monticolo Davy, E-mail: davy.monticolo@univ-lorraine.fr
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Abstract

Innovation and creativity are a mandatory for companies who wish to stay competitive. In order to promote an inventive dynamic, it implies to set up tools, habits, and an adapted environment to foster creativity. Creativity is the wealth of companies that should be valorized. To promote creativity, companies implement creativity workshops that gather people with various roles and expertise exchange and create knowledge to solve collectively open-ended engineering problems. However, group dynamics or facilitation can make the wrong decision and make the creative problem-solving unfruitful. The aim of our research project is to create a digital system to manage and valorize knowledge during creativity workshops. To design this system, we need to formalize the knowledge domain of creative workshops. The ontologies are used for decades to structure and manage information and knowledge in different domains. However, methodologies to design these ontologies are either hardly reproducible or not oriented to extract knowledge from organization. This article describes a methodology based on an organizational modeling to build ontologies. We will illustrate our approach by designing an ontology that models knowledge of creativity workshops.

Keywords

Creative workshopinnovationknowledge engineeringontologyorganization
Type
Research Article
Information
AI EDAM , Volume 33 , Special Issue 2: Knowledge Engineering and Management Applied to Innovation , May 2019 , pp. 148 - 159
Copyright
Copyright © Cambridge University Press 2019 

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