Our research in Knowledge Graphs and Linked Data focuses on structuring and connecting heterogeneous data to enable intelligent, interoperable, and context-aware systems. We develop scalable methods for semantic modelling, reasoning, and integration that support open, dynamic, and trustworthy data ecosystems. Embracing the dataspace paradigm, we aim to facilitate flexible data collaboration while preserving autonomy, privacy, and data sovereignty. Our vision is to drive data-driven innovation by transforming fragmented information into connected, meaningful, and actionable knowledge. ​​

Current Projects: 

  • An Integrated Graph Theoretical Substructure Similarities Searching Algorithm for Drug Repositioning and Off-Target Toxicity Assessments using Antimicrobial Resistance Model - Details - Dec 2022 – Nov 2025

United Nations Sustainable Development Goals (UNSDGs)

SDG 3SDG 4SDG 8SDG 9SDG 11SDG 13