## 4 Discussion
Developing a BWMD ontology module based on the PMD-Core mid-level ontology will be advantageous in leveraging the synergies between both ontologies. Furthermore, another ontology, the MSEO [22], developed as part of the Materials Open Laboratory initiative, also aligns with the BFO principles. The MSEO uses the Common Core Ontologies (CCO) [59] as its framework, and the community involved in its development has worked to integrate most of the BWMD concepts into itsdomain vocabulary. This effort is demonstrated by 977 mappings between the MSEO and BWMD [60]. Besides, a mechanical testing domain module [24] was created within the Urwerk project [61], partially covering the MSE domain. Creating mappings between these MSE ontologies can enhance interoperability and mutual benefits. It is worth mentioning that showcasing the application of the Graph Designer Workflow with the BWMD Ontology was a merely practical decision, as the fitting ontology solution was already available for semantically describing the ASi10 Mg dataset. However, transferring all the necessary domain concepts to a different mid-level ontology, such as the PMD-Core Ontology, is not an issue. In addition to the Graph Designer Tool, other digitalization tools are available for material data. Tools like OpenRefine [62], TARQL [63], and Ontopanel [50] offer different approaches for digitalizing and structuring MSE data. However, the advantages of the Graph Designer Workflow, as presented in this work, are significant. The Graph Designer Tool [64] provides a user-friendly solution for uploading ontology vocabularies into a generic Process Graph template, managing multiple namespaces, and converting the Graph Template into a standard Excel Table for data annotation. This minimizes errors from misinterpreting domain vocabulary and allows domain experts to populate the Process Graph template with real data. The proposed MSE data digitalization strategy successfully achieves the goal of bridging the existing knowledge gap between domain and ontology experts. While not technological, this challenge poses significant obstacles in digitalizing material science knowledge. Effective information exchange between domain and ontology experts is crucial for designing appropriate semantic models. Understanding the meaning of classes when incorporating new vocabulary into the domain ontology is essential for selecting suitable upper classes from mid-level or top-level ontologies. The quality of this exchange will determine the value of the generated semantically structured data, with a richer vocabulary providing deeper context and knowledge. The interview template introduced in Section 2.3.1 facilitates efficient information exchange, guiding the classification of new concepts according to the semantic schema of the generic Process Graph template in Section 2.3.2. This significantly aids ontology experts in adjusting templates and creating compliant data models. The challenges in knowledge extraction are often overlooked in literature despite being well-known in the ontology development community. Additionally, using NLP (Natural Language Processing) to extract information from domain-specific literature is a powerful tool for semi-automated domain ontology creation. The generic aspect of the Graph Designer Workflow implies its possible application with other ontologies or semantic data models. The presented work demonstrates its applicability based on the BWMD Ontology and the generic Process Graph template as a semantic data model to efficiently build digital process chains for the cast ASi10 Mg use case. The BWMD Dataset RDF graph, depicted in Figure 10, has been introduced in Section 3. This RDF graph is publicly available in the Fordatis database [33]. The primary objective of this work is to present a generic digitalization method for semantically modeling various processes using different ontologies as base vocabularies and instantiating schemas or generic Process Graph templates.