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  4. Enhancing Data Space Semantic Interoperability through Machine Learning: a Visionary Perspective
 
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2023
Conference Paper
Title

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

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.
Author(s)
Boukhers, Zeyd  
Fraunhofer-Institut für Angewandte Informationstechnik FIT  
Lange-Bever, Christoph  orcid-logo
Fraunhofer-Institut für Angewandte Informationstechnik FIT  
Beyan, Oya Deniz
Fraunhofer-Institut für Angewandte Informationstechnik FIT  
Mainwork
The ACM Web Conference 2023. Companion of The World Wide Web Conference WWW 2023  
Conference
World Wide Web Conference 2023  
DOI
10.1145/3543873.3587658
Language
English
Fraunhofer-Institut für Angewandte Informationstechnik FIT  
Keyword(s)
  • data spaces

  • machine learning

  • semantic interoperability

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