## 1 Introduction
Furthermore, basic tasks and concepts can be found in all development processes, regardless of the individual company or developed product. Besides the process itself, the available data and the used methods, the Digital Engineering team has a major influence on the subsequent success. Especially, method-specific knowledge in a team is identified as a crucial factor [26]. An ontology could help by linking the area of product development and the existing fields of application with Digital Engineering methods. An ontology is defined as an 'explicit specification of a conceptualisation' [27], providing domain-relevant concepts and other relevant objects and relations between them. The power of those ontologies has been shown earlier in different, mainly experiment-intensive use cases such as tribology [28] or gene-bases [29]. Although initial approaches towards a Digital Engineering ontology exist, they mainly focus on individual aspects or closed subdomains. At the beginning of the product development process, [PERSON] et al. managed customer needs in product development by an ontology-based structure [30]. During the following part and assembly design, collaboration became more and more relevant in the changing work environments. Therefore, an ontology-supported framework can resolve assembly constraints and lead to a standardisation of design [31]. The next step in the development process would be the simulation-based validation of designs. Since simulation experts are rare, automation would be helpful. Therefore, the ontology presented by [PERSON] et al. [32] provides a structured vocabulary for the interconnection of simulation knowledge obtained through text mining and building simulations based on it. Since product knowledge can become obsolete, forgetting is a crucial part of knowledge management and can be supported by ontologies as well [33]. For a more general view of product development, besides individual use cases in the process, [PERSON] et al. [34] presented an ontology representing and managing the product development process. Furthermore, for structuring data-driven methods in the context of product development, the first semantic web application exists [20]. Besides these engineering-based ontologies, descriptions of abstract processes are realised in the PROV-ontology (PROV-O) [35], and persons and teams can be made by the friends-of-a-friend (FOAF) ontology [36]. Several ontologies in the area of product development and data-driven methods exist. On the downside, they are not sufficiently connected to link the segregated areas. However, this link is needed to support the identification of suitable data-driven methods for a given task in the product development process that should be automated. Therefore, the innovation here is the establishment of this linkage, which was not available before. The central goal of this contribution is a new ontology that connects data-driven methods and tools with the product development process to support the implementation of Digital Engineering in SMEs. Therefore, the input and output data of the individual processes should be represented as well as the involved employees. Both aspects influence suitable methods and tools since they require different input data formats or enable several outputs. Furthermore, the individual knowledge of employees limits the tools that can be used, as they are needed to program or use command line tools. The presented problems and gaps in the existing studies lead to the following research question:How can the domains of artificial intelligence and product development be linked to simplify the introduction of Digital Engineering in small and medium-sized enterprises by improving method knowledge availability? To answer this, the remaining contribution is structured as follows: In Section 2, the functionality of AI4 PD is shown after the presentation of an initial use case in Section 2.1.