Conference Papers
1. Petrova-Antonova, D., Pareti, P., Tomov, P., Yumusak, S., Maidens, C., Jiang, S., ... & Roman, D. (2025, October). Knowledge Graph-Driven Policy Enforcement in Urban Dataspace. In International Joint Conference on Knowledge Graphs (pp. 406-421). Singapore: Springer Nature Singapore.
This article presents an innovative approach to managing and implementing policies in urban data spaces through the use of knowledge graphs and semantic technologies. The research is motivated by the growing complexity of smart cities, where numerous stakeholders exchange data from heterogeneous sources under dynamic conditions and in real time. The authors analyse the key challenges facing data governance in urban data spaces, including the multi-party management of data access and usage rights, the resolution of policy conflicts, and the need for context-dependent and spatiotemporal enforcement of data usage rules. In response to these challenges, an extension to an existing policy framework based on the Open Digital Rights Language (ODRL) is proposed, which uses knowledge graphs to ensure semantic interoperability and automated reasoning during policy validation and enforcement. The presented architecture includes mechanisms to support spatiotemporal constraints, dynamic participants, and IoT context, as well as capabilities for using large language models to assist non-technical users in creating and interpreting policies. The results demonstrate the potential of combining knowledge graphs, ODRL, and LLM technologies to build reliable mechanisms for access control and compliance with regulations in real-world urban data spaces, while also laying the groundwork for future developments in the field of automated data management and digital ecosystems.
2. Bankov, B. (2025, November). Data Spaces Against Hybrid Warfare: A Novel Way to Achieve Resilience. In 2025 International Conference on Big Data, Knowledge and Control Systems Engineering (BdKCSE) (pp. 1-8). IEEE.
This article presents the results of research conducted as part of the project, which focused on analysing the role of data spaces as a mechanism for building resilience in complex, dynamic, and critical environments. The publication develops a conceptual framework that demonstrates how modern models for data management and sharing can support coordination among multiple independent actors while ensuring data security, trust, and sovereignty. The study identifies the main challenges associated with information exchange between different organizations and domains and analyses the potential of data spaces to ensure interoperability, effective event detection, and improved decision-making under conditions of high uncertainty. As a key outcome of this work, a model is proposed for using data spaces as a foundation for building reliable ecosystems for data exchange and analysis, capable of supporting early risk detection, strategic forecasting, and interorganizational collaboration. The developed concept emphasizes the importance of semantic interoperability, federated data governance, and access control mechanisms as key prerequisites for the effective use of data in distributed environments. The results obtained contribute to the advancement of scientific research in the field of data spaces and lay the foundation for future developments in intelligent services for data management, integration, and analysis, which are directly aligned with the project’s goals of building reliable and sustainable digital ecosystems.