Product Life Cycle Management (PLM) strategies have been proven as recommended approach for handling, planning and decision making regarding all aspects of products different life cycle stages. As this domain includes number of actors, system platforms and technologies, gathering information and handling knowledge often emerges as challenging problem. In this paper we propose a methodology to support a PLM approach taking advantage from the emerging semantic web technologies and the underlying paradigm of Linked Data. The main purpose of this methodology is twofold. First, it aims at providing a semantic model for handling different data sources and data types, structuring thus information from all stages of a product life cycle. Second, it extends reasoning mechanisms offered by semantic technologies, to conduct comparative and in-depth data analysis based on data mining and pattern discovery.