Enhancing Data Space Semantic Interoperability through Machine Learning: a Visionary Perspective
This addresses inefficiencies in data exchange for stakeholders in data spaces, but it is incremental as it builds on existing concepts without presenting new results.
The paper tackles the problem of manual metadata management in data spaces, which is time-consuming and error-prone, by proposing a vision to use machine learning for automatic metadata generation to improve semantic interoperability.
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 metadata 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 significantly improved. This involves automatically generating and updating metadata, which results in a more flexible vocabulary that can accommodate the diverse terminologies used by different 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.