Enhancing Data Space Semantic Interoperability through Machine Learning: a Visionary Perspective

Our vision paper outlines a plan to improve the future of semantic interoperability in data spaces through the application of machine learning. The use of data spaces, where data is exchanged among members in a self-regulated environment, is becoming increasingly popular. However, the current manual practices of managing meta-data and vocabularies in these spaces are time-consuming, prone to errors, and may not meet the needs of all stakeholders. By leveraging the power of machine learning, we believe that semantic interoperability in data spaces can be signifcantly improved. This involves automatically generating and updating metadata, which results in a more fexible vocabulary that can accommodate the diverse terminologies used by diferent sub-communities. Our vision for the future of data spaces addresses the limitations of conventional data exchange and makes data more accessible and valuable for all members of the community.