## 5 Conclusions
Here, users can instantiate data in a flat hierarchy, unlike the requirement to preserve data hierarchy in workflows based on schema languages such as LinkML [67] and JSON-Schema. These advantages have led to the adoption of the Graph Designer Workflow in digitalization efforts involving diverse materials and processing steps. Examples include AluTrace (referenced as the Fraunhofer IWM toolchain [55; 68]), iBain (dilatometry, isothermal bainitic transformation, axial and bending resonance fatigue tests of bainitic steel [56]), Urwerk (process chain of 100 Cr6 steels including heat treatment, surface finishing, and fatigue tests [61]), H2 Digital (hydrogen fatigue test [69]), etc. * A simple and interpretable machine learning model illustrates the benefit of creating semantic-rich MSE data. A Decision Tree Regression model is used to predict the maximum tensile strength as a function of heat treatment and chemical composition. This work successfully accelerates materials discovery by digitalizing data from material process chains using a generic methodology. The resulting knowledge graphs adhere to the FAIR principles and provide a valuable resource for materials-domain experts. Future work will focus on addressing the challenges of semantic data modeling and further improving user experience. Conceptualization: C.S., P.v.H., A.T. and E.G.T. ; methodology: C.S., P.v.H., A.T., M.J.[PERSON] and E.G.T. ; software: P.v.H. and A.T.; validation: C.S., P.v.H., A.T. and E.G.T. ; formal analysis: C.S., P.v.H., A.T. and E.G.T. ; investigation: C.S., P.v.H., A.T. and E.G.T. ; resources: C.S., P.v.H., A.T. and E.G.T. ; data curation: C.S., P.v.H., A.T. and E.G.T. ; writing--original draft preparation: E.G.T., C.S., A.T. and [PERSON]-R.; writing--review and editing: [PERSON], C.S., P.v.H. and A.T.; visualization: [PERSON], [PERSON], [PERSON] and [PERSON]; supervision: [PERSON], [PERSON] and [PERSON]; project administration, C.S. ; funding acquisition, C.S. All authors have read and agreed to the published version of the manuscript. The study has received funding from the 'Ministerium fur Wirtschaft, Arbeit und Wohnungsbau Baden-Wurttemberg' under the project MaterialDigital, funding number 3-4332.62-IWM/21. Not applicable. The complete BWMD dataset, including two Jupyter Notebooks with integrated SPARQL queries to directly interact with the graph data, is published and available for download under MaterialDigital Dataset accessed on 5 December 2024. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} FAIR & Findable, Accessible, Interoperable, and Reusable \\ AI & Artificial Intelligence \\ IT & Information Technology \\ ICSD & Inorganic Crystal Structure Database \\ OQMD & Open Quantum Materials Database \\ MDF & Materials Data Facility \\ NOMAD & Novel Materials Discovery \\ MSE & Materials Science and Engineering \\ W3C & World Wide Web Consortium \\ BFO & Basic Formal Ontology \\ OWL & Web Ontology Language \\ LLMs & Large Language Models \\ IAO & Information Artifact Ontology \\ RDF & Resource Description Framework \\ EU & European Union \\ SLACKS & Semantic Linked Data Stack \\ MMKG & Materials Modeling Knowledge Graph \\ BWMD & Baden Wüttemberg Material Digital \\ SPARQL & SPARQL Protocol and RDF Query Language \\ PMD & Platform Material Digital \\ TARQL & SPARQL for Tables \\ NLP & Natural Language Processing \\ GUI & Graphical User Interface \\ JSON & JavaScript Object Notation \\ \end{tabular}