## 1 Introduction  
In materials science and engineering (MSE), an interdisciplinary field that focuses on the properties, performance, and applications of materials [1], establishing a well-conceived, reliable, and consistent methodology for producing FAIR (Findable, Accessible, Interoperable, Reusable) material data [2] is essential for accelerating material discovery. Adhering to FAIR principles enhances reproducibility and reduces data ambiguity, which is particularly important in a domain such as MSE, characterized by data scarcity and diversity. The World Wide Web Consortium (W3C) [3] has been instrumental in developing and promoting Semantic Web standards, which include RDF (Resource Description Framework), OWL (Web Ontology Language), and SPARQL (a query language for RDF data) [4]. These standards offer a framework for meaningfully sharing and connecting data across the web, enabling interoperability among various applications and domains. Adopting these standards to transform materials data into knowledge graphs represents a promising approach to generating FAIR data in MSE, facilitating the direct linkage of data produced at various stages of materials processing chains. Furthermore, with the advent of powerful large language models (LLMs) [5], knowledge graphs may serve as a countermeasure to address the performance limitations identified within the MSE domain [6]. For instance, strategies that ground the outputs of LLMs in the factual content of knowledge graphs could significantly enhance their reliability [7]. The present work is motivated by the need for a clear and systematic digitalization workflow that generates high-quality, semantically structured linked material data. To this end, a user-friendly approach for digitalizing material data is introduced, aiming to ensure that the proposed solution can be implemented not just by IT experts but also materials engineers and scientists from diverse backgrounds. In prescribing a digitalization strategy in MSE, various data sources must be considered, including structured and unstructured data collections and actively generated data in research and production. The examination of existing datasets, such as the Inorganic Crystal Structure Database (ICSD) [8], the Open Quantum Materials Database (OQMD) [9], the Materials Project [10], the Materials Data Facility (MDF) [11], and the Novel Materials Discovery (NOMAD) [12], reveals significant deficiencies. Many of these databases contain incomplete and ambiguous data points and lack interconnectivity. This issue is exacerbated by data silos, where information is stored in isolated systems, making access and integration challenging. The diverse sources and formats of data, often in natural language, render much of it untraceable and unstructured, limiting the discoverability, retrievability, and reusability of data for both research and industry. These issues are well highlighted in the work of [PERSON] et al. [13]. Additionally, the absence of standardized data formats and protocols presents a critical challenge [14]. The diversity of research areas within the materials science community complicates seamless integration and analysis, resulting in fragmented datasets that are difficult to compare or combine. For instance, integrating experimental and computational data remains complex, as the field generates vast amounts of data from various synthesis and characterization experiments. The Materials Experiment and Analysis Database (MEAD) [15] has been developed to manage millions of materials synthesis and characterization experiments; however, the sheer volume and diversity of the data pose substantial challenges for effective integration and analysis. The challenges posed by data silos, lack of standardization, and difficulties in integrating disparate datasets significantly slow down progress in materials science. These issues hinder the ability to connect material data with contextual information and complicate the analysis of large amounts of information.