## 1 Introduction
Addressing these challenges is essential for advancing the field and unlocking new opportunities for discovery and innovation. Semantic web technologies provide a promising solution, ensuring material data is structured, interconnected, and interoperable. In the stack of W3C-endorsed technologies, ontologies are fundamental in adding meaning to data, ensuring it is unambiguous and interoperable [16; 17; 18]. They serve as'machine-understandable' representations of a domain, where knowledge is expressed as axioms that can be used to infer new insights from existing data, thereby improving data querying and analysis. Numerous ontology development efforts have been undertaken to model different subdomains as well as the general MSE domain. Most of these ontologies either do not adhere to a top-level ontology or utilize BFO [19] or EMMO [20] as the top-level. In the general MSE domain, ontologies like PMD-Core [21], MSEO [22], and EMMO are currently popular. Additionally, ontologies have been developed for various subdomains and applications in MSE, such as mechanical testing [23; 24; 25] and manufacturing [26; 27]. [PERSON] et al. [28] highlight that despite numerous ontology design efforts in MSE, there is still a need to adopt ontology design patterns based on explicit competency question formulations in the MSE domain. This work presents a mid-level ontology that captures generic concepts in the MSE field. It also includes a domain-specific ontology focused on the permanent mold casting process for AlSi10 Mg, along with the associated manufacturing and material characterization processes. The digitalization strategy has been intentionally designed to be ontology-agnostic, facilitating collaboration with other ontology development efforts in MSE. Despite numerous initiatives to develop ontologies in MSE, a lack of focus on practical methods for data annotation and user-friendly workflows that can assist materials scientists in utilizing Semantic Web technologies to produce FAIR data remains. While some related work has been discussed, including a well-documented example of a web-baseddata infrastructure for generating FAIR tribological data [29], it is clear that a formalized knowledge extraction process is needed to improve communication between ontology developers and materials science experts. Bridging the gap between these disciplines is essential for increasing the acceptance of ontologies in materials science. Additionally, it is paramount to showcase the feasibility and applicability of digitalization methods using real-world examples. This is necessary not only to validate and enhance existing ontologies and knowledge graph structures but also to showcase the knowledge gained from digitizing materials science data using Semantic Web technology. A compelling application of generating semantically structured materials data lies in providing context-rich and historically informed data to AI models, which can significantly accelerate material design and optimization processes. The main objective of this work is to improve the efficiency and effectiveness of digital mapping of chemistry-process-structure-property relationships along industrially relevant process chains through a straightforward and systematic digitalization workflow that generates high-quality, semantically structured, linked materials data. A key focus is building digital process chains, which are essential for managing the product life cycle of any object and ensuring adequate quality control. A methodology for creating digital process chains is particularly critical in engineering, where existing literature has not adequately addressed this need. Consequently, significant efforts have been made to implement the necessary digitalization steps in a user-friendly manner, ultimately contributing to the advancement of digitalization in MSE data. This investigation introduces a methodology to structure and uniformly represent material data along real-world process chains.