1. Introduction
The main goal of Product Lifecycle Management (PLM) is the management of all the business processes and associated data generated by events and actions of various lifecycle agents (both human and software systems) and distributed along the product's lifecycle phases: Beginning of Life (BOL) including design and manufacturing, Middle of Life (MOL) including usage and maintenance and End of Life (EOL) including recycling, disposal or other options [1]. A major requirement for efficient PLM is the traceability of the product which is the acquirement of information along the product's lifecycle about the product. Furthermore, making this information “smart” instead of “dump” is a key aspect of future systems aiming to boost performance in data management and in their transformation to information and to knowledge. Extracting knowledge in order to improve features of products and of future products is a very promising target field of using this information. A big amount of this information-knowledge is being lost, due to lack of reasoning capabilities as well as lack of interoperability and integration of elements of today's PLM systems and models.