Analysing semantic data storage in Distributed Ledger Technologies for Data Spaces

TL;DR

This paper systematically evaluates how different types of distributed ledger technologies handle semantic data storage in data spaces, highlighting efficiency, resource use, and suitability for various data sovereignty needs.

Contribution

It provides a comparative analysis of public, private, and hybrid DLTs for semantic data storage, filling a gap in understanding their performance in real-world knowledge graph applications.

Findings

Private DLTs are most efficient for semantic data storage.

Hybrid DLTs balance auditability and operational efficiency.

Performance varies significantly across DLT types.

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.