Masterarbeit
A Low-Code/No-Code Interface for Academic Knowledge Graphs
Completion
2025/08
Research Area
Intelligent Information Management
Students
Ranjitha Sethumadhavan
Advisers
Description
The increasing adoption of Knowledge Graphs (KGs) in academia provides new opportunities for organizing and retrieving information in a structured fashion. Access to Knowledge Graphs is commonly provided through SPARQL endpoints, which allow users to query and explore the data. However, constructing SPARQL queries requires technical expertise, limiting access to users unfamiliar with the query language. This represents an obstacle for non-technical users, preventing them from leveraging the full potential of Knowledge Graphs. Current approaches either require manual query writing or provide limited graphical interfaces. As a result, there is a demand for intuitive Low-Code/No-Code (LCNC) approach that empowers users with little to no programming experience to interact with Knowledge Graphs effectively.
The objective of this thesis is to develop a user-friendly, Low-Code/No-Code interface specifically for academic Knowledge Graphs that does not require any knowledge of query languages on the user side. To this end, the first step is to conduct a requirements analysis and to review graphical query builders, form-based query generation, or other interactive methods that bridge the gap between domain experts and Semantic Web technologies. Existing solutions need to be classified and evaluated according to the identified requirements. Finally, a Low-Code/No-Code interface for academic Knowledge Graphs has to be conceptualized, implemented and assessed in a suitable evaluation.
