Analysing semantic data storage in Distributed Ledger Technologies for Data Spaces

2025-07-03

AI Summary

Semantic interoperability in data spaces necessitates efficient storage and management of semantic data, yet the suitability of distributed ledger technologies (DLTs) for this purpose remains underexplored. Method: This study conducts the first systematic empirical evaluation—using real-world knowledge graphs—of public, private, and hybrid DLTs across query latency, update throughput, resource consumption, and support for semantic operations. It further proposes a data sovereignty–driven DLT architecture selection framework. Contribution/Results: Private DLTs demonstrate superior efficiency for semantic data management, while hybrid DLTs achieve the optimal trade-off between auditability and execution performance. Public DLTs exhibit significant overhead and limited semantic expressiveness. These findings provide the first evidence-based guidance and methodological framework for DLT selection in data space infrastructure design, directly informing architectural decisions grounded in functional requirements, governance models, and operational constraints.

Technology Category

• Data Mining & Knowledge Management: Semantic Web
• Knowledge Representation and Reasoning: Description Logics
• Search and Optimization: Distributed Search

Application Category

• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Security and Privacy: Blockchains and distributed ledgers
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

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.

Problem

• Evaluating semantic data storage in distributed ledgers for data spaces
• Comparing DLT types for performance and efficiency in semantic data handling
• Determining optimal DLT infrastructure for decentralized data ecosystems

Innovation

• Private DLTs efficiently store semantic data
• Hybrid DLTs balance auditability and efficiency
• Semantic web technologies enhance interoperability