The field of materials science is undergoing a transformative shift driven by digitalization. Semantic and AI technologies are advancing materials development, design, and optimization within an Industry 4.0 environment. Addressing quality assurance and data interoperability, this presentation examines the integration of semantic technologies and knowledge representation methods. By adhering to FAIR principles, this approach enhances data management, storage, and reuse, fostering both machine-actionable and human-understandable data structures crucial for digital research environments.

This presentation focuses on the ‘platform MaterialDigital’ (PMD) initiative, which supports efforts from both industrial and academic sectors to solve digitalization challenges and implement sustainable digital solutions. PMD aims to create virtual material data spaces and develop solutions for systematizing and unifying the handling of hierarchical, process-dependent material data. Semantic technologies play a crucial role in these efforts, enabling the storage, processing, and querying of data in a contextualized form. The development and application of the PMD Core Ontology 3.0 (PMDco 3.0) tailored for materials science is highlighted, including the design and documentation of graph patterns compileable into rule-based semantic shapes. Its integration into daily lab life is demonstrated through its application to electronic lab notebooks (ELN), illustrating the potential of standardized protocols and automation-ready solutions for managing diverse experimental data across different sources.

Outlining best practices and illustrating the possibilities that semantic technologies bring to modern labs, examples from materials processing and mechanical testing will underscore how knowledge graphs bridge the gap between data and decision-making in materials science, with potential for increased productivity and streamlined workflows across the field.