Thesis Projects
Information about Diploma/Master thesis process
Open Theses
Knowledge graphs like Wikidata combine rich relational structure with natural-language descriptions, yet most models are trained narrowly for a single task and transfer poorly. This thesis investigates how a single generative graph foundation model, pretrained on large-scale text-rich knowledge graphs, can be adapted to a range of downstream tasks, including knowledge graph completion, text-conditional subgraph ...
Large language models (LLMs) are increasingly used in biomedical applications, including literature mining (PMID: 40188094), drug discovery (PMID: 38730226; 41362614; https://arxiv.org/abs/2510.27130), clinical decision support (PMID: 40753316), and patient data analysis (PMID: 41034564). Hybrid approaches combining LLMs with structured knowledge bases and retrieval-augmented generation (RAG) improve performance and interpretability (PMID: 38830083; https://www.biorxiv.org/content/10.1101/2025.05.08.652829v2) . However, LLM-based systems ...
Running Theses
This thesis investigates how to develop a foundation-model-style representation learner for Academic Knowledge Graphs (AKGs), such as arXiv-, DBLP-, MAG-, and Open Academic Graph-derived data. Academic graphs connect papers, authors, venues, institutions, fields of study, keywords, and citations through typed relations that evolve over time, making them a natural setting for temporal heterogeneous graph representation ...
Knowledge graphs built automatically from documents inherit extraction errors: values get truncated, entities are dropped, or records are bound to the wrong document. Structural validators such as SHACL catch formatting violations, but they cannot answer the question that matters for curation: does the source document actually contain the fact the graph claims?
Large Language Models (LLMs) are increasingly used to translate natural-language requirements into machine-readable data usage policies. In the ODRL Multi-Agent LLM framework, such requirements are transformed into ODRL policies through a multi-agent pipeline supported by ontology-guided prompting and structured policy generation . However, LLM-based reasoning alone cannot provide formal guarantees about constraint consistency, particularly when ...
A Layered Architecture for Entity Resolution in Scholarly Metadata Combining Algorithmic and Agentic Approaches
This thesis investigates how compact, application-specific ontology modules can be extracted automatically from large ontologies, preserving the semantic coherence needed for downstream tasks while drastically reducing complexity.
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Completed Theses
Thesis Type
Master
Student: Sebastian Miller
Status
In Progress
Background
Supervisory control and data acquisition (SCADA) systems are increasingly connected through information and communication technologies, exposing smart grids to cyberattacks and operational disruptions. Conventional signature-based intrusion detection systems (IDSs) reliably identify known attacks but cannot detect previously unseen patterns, while statistical and machine-learning-based IDSs may achieve high detection rates but often ...
Large Language Models (LLMs) are increasingly used to support data wrangling, but their integration into interactive transformation workflows raises new challenges for auditability, reproducibility, and accountability. When users approve, reject, or refine LLM-generated suggestions, conventional data lineage systems often fail to capture why a change occurred, who was responsible for it, and which transformation produced ...
Knowledge-augmented multiple-choice question answering (MCQA) aims to improve robustness and factual grounding by integrating external structured knowledge (e.g., knowledge graphs) into language-model-based decision making. Current high-performing systems typically retrieve a local subgraph relevant to a question and candidate answers, then combine pretrained language representations with explicit graph reasoning modules.
This thesis investigates an alternative representation path: ...
A Framework for Automated Sanitization of Cybersecurity Artifacts
Federated Machine Learning Architecture for an MDF Production Industry Use Case
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