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The use of ontologies in the context of product lifecycle management (PLM) is gaining importance and popularity, while at the same time it generates a lot of controversy in discussions within scientific and engineering communities. Yet, what is ontology? What challenges have been addressed so far? What role does ontology play? Do we really need ontology? These are the core questions this paper seeks to address. We propose to conduct a comprehensive study of the concept of Ontology firstly in its domain of origin, Philosophy, and secondly in information science. Based on the understanding of this concept and an in-depth analysis of the state of the art, seven key roles of ontology are defined. These roles serve as a framework describing the general state of research on the use of ontologies in the context of PLM.

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The context and problem of identifying and thereafter representing, analyzing and managing information and knowledge about an organization has always been very crucial to achieve business goals in an efficient and flexible way. Particularly in a PLM context, the issue of information overload is growing in importance. An emergent challenge consists in providing a contextdriven access to federated information and knowledge and fostering crossdiscipline collaborations between actors to improve quality in product development. This paper highlights key issues for knowledge definition and representation. We propose a bottom-up approach based on the User Story Mapping method (USM). This method is user-centric and leads to the definition of current and/or expected scenarios and processes along with a collaboratively agreed vision. Common concepts and viewpoints are therefore derived and generalized through a process of merging defined roles, activities and usages sequences with a focus on the product content. This bottom-up approach provides a federated and common understanding of information throughout the industrial product and process lifecycle; which combined with appropriate tools and methods, such as questionnaires, standards specifications, knowledge based approaches, etc. results in the definition of the knowledge network and domain and therefore improves capabilities for sharing and reusing this knowledge in collaborative product development. The proposed approach is applied in the context of the FP7 European project LinkedDesign (Linked Knowledge in Manufacturing, Engineering and Design for Next-Generation Production) based on three application scenarios.

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In this article, a system for automated data analysis and data modelling is presented. It is a well-known fact that a fully automated, data independent data mining system cannot be designed. It is proposed here that data from the product lifecycle management (PLM) domain can be successfully modelled mathematically by exploiting the advantages of having a semantic model of data, relaxed time constraints and by allowing sub-optimal accuracy. A data mining procedure is followed through in detail and, for each step, it is argued how the proposed assumptions tackle the challenges of automation. Finally, an industrial use case is presented to demonstrate the value gained from such a system.