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R&D: Analysing Semantic Data Storage in Distributed Ledger Technologies for Data Spaces

Research leads to discussion on selection of most appropriate DLT infrastructure based on data sovereignty requirements of decentralised data ecosystems.

arXiv has published an article written by Juan Cano-Benito, Data and Web Science Group, Universität Mannheim, Germany, Andrea Cimmino, Ontology Engineering Group, Universidad Politécnica de Madrid, Spain, Sven Hertling, Heiko Paulheim, Data and Web Science Group, Universität Mannheim, Germany, and Raúl García-Castro, Ontology Engineering Group, Universidad Politécnica de Madrid, Spain.

Abstract: Data spaces are emerging as decentralised infrastructures that enable sovereign, secure, and trustworthy data exchange among multiple participants. To achieve semantic interoperability within these environments, the use of semantic web technologies and knowledge graphs has been proposed. Although distributed ledger technologies (DLT) fit as the underlying infrastructure for data spaces, there remains a significant gap in terms of the efficient storage of semantic data on these platforms. This paper presents a systematic evaluation of semantic data storage across different types of DLT (public, private, and hybrid), using a real-world knowledge graph as an experimental basis. The study compares performance, storage efficiency, resource consumption, and the capabilities to update and query semantic data. The results show that private DLTs are the most efficient for storing and managing semantic content, while hybrid DLTs offer a balanced trade-off between public auditability and operational efficiency. This research leads to a discussion on the selection of the most appropriate DLT infrastructure based on the data sovereignty requirements of decentralised data ecosystems.

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