Product Lifecycle Management (PLM) is a strategic product-centric, lifecycle-oriented and information-driven business approach that strives to integrate people and their inherent practices, processes, and technologies, both within and across functional areas of the extended enterprise from inception to disposal. The integration of people relies on the harmonisation of domain-specific glossaries by standardising a universal PLM vocabulary. So far, unfortunately, there is no PLM standard vocabulary. Therefore, the tremendous amount of knowledge that is continually brought forward by academic research studies, industrial practices and computer-aided applications causes semantic ambiguities. This paper consists of an illustrated glossary and a conceptual map. The glossary identifies, discusses, clarifies and illustrates ambiguous terms used in discrete

The design of complex engineered systems highly relies on a laborious zigzagging between computeraided design (CAD) software and design rules prescribed by design manuals. Despite the emergence of knowledge management techniques (ontology, expert system, text mining, etc.), companies continue to store design rules in large and unstructured documents. To facilitate the integration of design rules and CAD software, we propose a knowledge graph that structures a large set of design rules in a computable format. The knowledge graph organises entities of design rules (nodes), relationships among design rules (edges), as well as contextual information. The categorisation of entities and relationships in four subcontexts: semantic, social, engineering, and IT -facilitates the development of the data model, especially the definition of the "design context" concept. The knowledge graph paves the way to a context-aware cognitive design assistant. Indeed, connected to or embedded in a CAD software, a context-aware cognitive design assistant will capture the design context in near real time and run reasoning operations on the knowledge graph to extend traditional CAD capabilities, such as the recommendation of design rules, the verification of design solutions, or the automation of design routines. Our validation experiment shows that the current version of the context-aware cognitive design assistant is more efficient than the traditional document-based design. On average, participants using an unstructured design rules document have a precision of 0.36 whereas participants using our demonstrator obtain a 0.61 precision score. Finally, designers supported by the design assistant spend more time designing than searching for applicable design rules compared to the traditional design approach.