Topics: Knowledge Graphs, Information Visualization, Machine Learning
Supervision: Christian Steinparz, Marc Streit
Type: BSc Practicum, BSc Thesis, MSc Practicum, MSc Thesis
Knowledge graphs are widely used to explicitly model and represent knowledge in various domains including semantic web, natural language, and biomedical data. However, due to the potentially large number of nodes and edges, the visualization and navigation of knowledge graphs prove to be challenging. They require the use of advanced techniques such as user interactivity, aggregation, automated analysis, machine learning, and the combination thereof.
Projects in this area can address tasks such as:
- prototyping a visualization workflow for the exploration of KGs
- exploring the use of machine learning techniques to identify and extract relevant information from KGs
- experimenting with advanced ways of positioning nodes and edges such as KG embedding and down-projection
- visualizing constraint violation information in KGs
- applying interpretable ML techniques to predict information in the KG and utilizing the model explanations
- comparison of KGs by developing visual fingerprints or by applying ML-based similarity prediction
- visualizing provenance data of how KGs have changed over time (due to update/insert/delete of triples)
Students are encouraged to propose their own ideas in relation to the topic.