## 4 Discussion  
While many papers have focused on developing material ontologies [13; 44; 45; 46; 47], fewer have addressed the design of knowledge graphs for structuring material data through knowledge graph patterns (e.g., [48; 49]). Many examples of structuring MSE process data focus on ontology-based semantic frameworks designed to digitize a specific process. For instance, PMD-Core provides an ontology-based semantic data model for digitizing tensile tests with the Tensile Test Ontology [47]. Similarly, MSEO offers a data schema for semantically modeling hardness measurements [50]. Moreover, PMD-Core [51] supplies a framework for connecting processes with relevant (meta)data. This framework consists of a coarse-grained structure intended to chronologically connect processes and associate them with their input and output objects. So far, no investigations have concentrated on developing a fined-grain and generic method for digitizing material data using a semantic data model as a core pattern. This is the objective of the generic Process Graph template introduced in Section 2.3.2. This work aims to design a digitalization strategy focused on developing semantic data models for impartible processes, as outlined in Section 2. Despite the availability of existing material ontologies, an ontology specifically tailored for the digitalization strategy used to create digital process chains within the MSE domain was presented in Section 2.2. The BWMD Ontology employs BFO 2.0 as its top-level ontology, ensuring compatibility with the ISO/IEC 19763 standard [52]. This establishes a solid foundation for creating interoperable domain and application ontologies. The BWMD Ontology is the product of a two-year collaborative effort among MSE experts from various German research institutes initiated in 2018. Although this paper references its non-modularized version for simplicity, a modularized version exists and is published in [53]. This modularized version includes a mid-level module for general MSE concepts and a domain module for specific concepts related to mechanical testing procedures, microstructural features, and specimen preparation. Additional modules have been created to address various use cases beyond the AlSi10 Mg dataset. Extensions of the BWMD Ontology that describe other case studies or projects can be explored in [54]. For the AluTrace use case, an application ontology module was created to digitalize the Laser Bed Powder Fusion process to additively manufacture AlSi10 Mg and to discover optimal production routes (including different heat treatments) with the help of computer tomography scans, mechanical testing (tensile and fatigue), defect analysis and metal-lographic investigations. The AluTrace dataset is well-documented in [55]; individual process-graph patterns, RDF data, and SPARQL queries are available for download, as well as the BWMD-based ontology modules created to describe the use case. The AluTrace dataset exposes the versatility of the proposed framework. Nevertheless, this article does not advocate for a specific ontology to digitalize material data. Parallel developments of mid-level and domain-specific ontologies, such as the PMD-Core Ontology [21] from the Material Digital Platform initiative [56], are equally suitable for modeling MSE data. The PMD-Core Ontology was initially developed with a bottom-up approach to align with the EMMO [20] and has since been adapted for interoperability with BFO [51]. A unique advantage of the PMD-Core Ontology is that it is built on the W3C Provenance Ontology (PROV-O) [57]. This foundation provides a standardized framework for managing various data sources, facilitating data integration from different origins, and creating digital workflows [58]. While the BWMD Ontology manages data integration from diverse sources, it does not count with a specialized vocabulary for developing workflows. Conversely, the BWMD Ontology includes an extensive vocabulary for semantically describing the MSE domain.