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

ACM Web Conference (WWW), pp. 1462–1467

Abstract

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.

Authors 3

1. Zeyd Boukhers

   University of Cologne · University Hospital Cologne · Fraunhofer Institute for Applied Information Technology

   Affiliation as printed

   Fraunhofer Institute for Applied Information Technology, Germany and Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany

2. Christoph Lange Aachen Fraunhofer Institute for Applied Information Technology

   RWTH Aachen University · Fraunhofer Institute for Applied Information Technology

   Affiliation as printed

   Fraunhofer Institute for Applied Information Technology, Germany and RWTH Aachen University, Germany

3. Oya Beyan

   University of Cologne · University Hospital Cologne · Fraunhofer Institute for Applied Information Technology

   Affiliation as printed

   Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany and Fraunhofer Institute for Applied Information Technology, Germany