Achieving data interoperability is a critical challenge in the increasingly complex landscape of the nanosafety ifeld, where ensuring the safe use of nanomaterials is of great importance. The unique properties of nanomaterials, stemming from their size and structure, necessitate comprehensive and standardized data to evaluate potential risks and hazards. One significant challenge lies in the diversity of experimental approaches, measurement techniques and exchange formats employed in nanosafety research [1]. Fortunately, semantic modeling coupled with linked-data knowledge graphs emerges as a powerful solution. Semantic modeling involves structuring data in a way that adds meaning and context to the information, facilitating better harmonization and standardization. Linked-data knowledge graphs take this a step further by establishing relationships between diverse datasets and their metadata, creating a web of interconnected information. That allows for better understanding and seamless data integration and exchange across diferent domains and applications. Moreover, the semantic approach inherently complies with the FAIR principles (Findable, Accessible, Interoperable and Reusable) [2] and covers several of its sub-principles. Thus, making the data more accessible and reusable for the community.
using semantic