KAI Thesis Topics

You can find the slides presented at the MSc AI Thesis event here. Most of the topics below can be investigated by either BSc or MSc AI students. We also welcome groups of students working on the same or similar topic.

If you are interested in one of the projects below, please contact the supervisor(s) listed to receive more information about the topics. Where available, have a look at the detailed description first. Also, keep in mind that all theses can be shaped to accomodate your interests. Do note however that the KAI group has clear expectations of their students. We want to make sure students know what to expect from us as their supervisors. We have prepared a short document which touches upon some important points like meetings, planning and writing of your thesis.

The filter menu below can be used to limit the amount of projects on display. If you click on the keyword, only projects with that particular keyword are displayed. The number indicates the number of projects we have to offer with that keyword.

KAI Thesis Topics

Stefan Schlobach

Ilaria Tiddi

Identifying ancestor disciplines of Hybrid Intelligence ( B.Sc./M.Sc. )

Exploring attacking arguments in the Computer Science Knowledge Graph (CS-KG) ( B.Sc./M.Sc. )

Simulation of a micro-surgery knowledge-based robot. ( B.Sc./M.Sc. )

Improving Gaze Detection for Robotics ( B.Sc./M.Sc. )

Building a common-sense object dataset for Robots ( B.Sc./M.Sc. )

Building a system to recognise item owners ( B.Sc./M.Sc. )

Explaining pilot actions with KGs ( M.Sc. )

Explaining robotic actions for airplane manufacturing ( M.Sc. )

Patrick Koopmann

Explaining Missing Entailments from Ontologies ( M.Sc./B.Sc. )

Explaining Entailments from Ontologies ( M.Sc./B.Sc. )

Automated Hypothesis Generation using ABox Abduction ( B.Sc. )

Learning Concept Descriptions from Examples ( M.Sc./B.Sc. )

Extracting Sub-Ontologies ( B.Sc. )

Optimizing Concept Expressions ( B.Sc. )

Ritten Roothaert

Jieying Chen

Bias in AI: Bias Detection and Mitigation in Large Language Models ( B.Sc./M.Sc. )

Leveraging Large Language Models for Ontology Extraction through Question-Answering ( M.Sc. )

Enhancing Language Models with Ontology Subsumption Inference ( M.Sc. )

Ontology Text Alignment Using Large Language Models (BERT and Generative LLMS) ( M.Sc. )

Designing a Benchmark and Auto-Evaluator for Extracting Relevant Axioms from User Input ( M.Sc. )

Extraction of Relevant Axioms Using Ontology Embedding ( M.Sc. )

Ethnic AI: Bias Detection and Mitigation in Large Language Models ( B.Sc./M.Sc. )

Benchmarking Ontology Modularity and QA Using Real-world Ontologies ( M.Sc. )

Ontology Embedding using the BERT Model ( M.Sc. )

Python-based Implementation of Ontology Modularity Tools ( M.Sc. )

Ameneh Naghdi Pour

Enhancing Ontological Reasoning with SWRL Rules: A Semantic Approach ( B.Sc. )

Vera Stebletsova

The field of application of Modal Logics to specification and verification of correctness of CS and AI systems, project 1 ( B.Sc./M.Sc. )

The field of application of Modal Logics to specification and verification of correctness of CS and AI systems, project 2 ( B.Sc./M.Sc. )

Daira Pinto Prieto

Knowledge Compilation for Forgetting Uncertain Evidence ( M.Sc. )

Uncertain Evidence in Artificial Intelligence ( B.Sc. )

Belief function networks for fault diagnosis ( B.Sc. )

Belnap-Dunn logic for fault diagnosis: an end-to-end solution ( M.Sc. )

Credal networks for fault diagnosis: What we know about the unknowns ( M.Sc. )

Epistemic modal logic for fault diagnosis ( B.Sc. )

Knowledge compilation for belief function: from theory to practice ( M.Sc. )

Multi-layer belief model: systematic comparison of evidence rules of combination ( M.Sc. )

Possibilistic networks for fault diagnosis ( B.Sc. )

Ranking networks for fault diagnosis ( B.Sc. )

Pelletreau-Duris Tom

Navigating information through networks ( B.Sc. )

Internships