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.
A first step towards achieving interoperability and therefore, into allowing data exchange between different platforms used by various lifecycle agents’ platforms, is the definition of a common-hierarchy data structure. The thorough control and distribution of information between different lifecycle agents and phases is the underlying goal for the PLM approach. Moreover, using new tools with additional reasoning capabilities prove to be very promising for facilitating future PLM systems.
An ontology model of the Product Data and Knowledge Management Semantic Object Model (SOM) [2] has been developed, with the aim of implementing ontology advantages and features into the model. The decision for the development of an ontology was inspired by the capabilities of this technology combining data structure layout, description logic reasoning and semantic web rules.
The structure of the paper is as follows. Section 2 demonstrates the state of the art of developed ontologies in several scientific fields. Section 3 describes briefly previous works on ontologies in PLM and the PROMISE SOM. In Section 4 a detailed description of the transformation of the SOM into an ontology is presented. In Section 5, a case study of the automotive industry facilitating reasoning capabilities is demonstrated.