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.
Cite
@article{arxiv.2303.08932,
title = {Enhancing Data Space Semantic Interoperability through Machine Learning: a Visionary Perspective},
author = {Zeyd Boukhers and Christoph Lange and Oya Beyan},
journal= {arXiv preprint arXiv:2303.08932},
year = {2023}
}
Comments
Accepted for publication @ The First International Workshop on Semantics in Dataspaces (In conjunction with The Web Conference - WWW 2023)