Ammar Ammar

Maastricht University

Fellowship project title: Developing training materials to construct and query linked data knowledge graphs using a low-code approach 

The why: The increasing amount of data generated in research fields requires efficient organization and analysis to answer critical research questions, and adherence to the FAIR principles to enable sharing and reuse. Knowledge graphs can facilitate this process by connecting existing data and knowledge entities while adopting open standards and semantic web technologies to make the data FAIR and machine-readable. However, constructing knowledge graphs using semantic web requires significant effort and technical expertise, creating a barrier for their wider adoption. This project aims to address this challenge by developing open training materials for constructing knowledge graphs using a low-code visual approach and covering use cases from different research disciplines. The project will bridge the gap between complex technology and scientific research by providing researchers with the knowledge and skills they require to construct knowledge graphs. The materials will be made available online, annotated with machine-readable metadata and published under an open license. 


Maastricht University page