Speaking Hours

Tuesday, 11:00 - 13:00

Always send an email to schedule an appointment before coming to the office. Without an appointment, I may not have time to meet with you or may not be in the office, even during office hours.

Thesis Supervision

I supervised/supported several bachelor, master and doctoral theses (sorted by the year of submission).

2019

• Learning Arbitrary RDF Dataset Enrichment Graphs Pre- & Postcondition Broadcasting (Master thesis)

2021

• OntoConnect: Domain-Agnostic Ontology Alignment using Neural Networks (PhD thesis)

2022

• Reinforced Active Learning for Link Discovery (Msc thesis)
• Link Discovery over geo RDF data: Content similarity approach (Bachelor thesis)

2023

• Leveraging Social Media in Disaster Situations (PhD thesis)

2024

• Geospatial Link Discovery With Human In The Loop (PhD thesis)

2025

• Developing Search Approach to Support Software Engineers Making Architectural Design Decisions (Msc thesis)

2026

• TIM: Tiered Iterative Knowledge Graph Matching (Msc thesis)

Research Interest

My research focuses on Knowledge Graphs, Semantic Web technologies, and Machine Learning, with an emphasis on scalable, explainable, and FAIR data integration and representation. I develop methods for link discovery, ontology and entity alignment, and knowledge graph construction, combining symbolic reasoning with neural and embedding-based approaches. My recent work explores adaptive and post-hoc alignment techniques for knowledge graph embeddings, including attribute- and structure-aware alignment methods, self-configuring matching frameworks, and approaches for entity alignment without retraining. I also investigate relation-aware alignment and large-scale embedding infrastructures for web-scale knowledge graphs. In parallel, I work on learning and reasoning in Description Logics, including belief-driven and structure-aware class expression learning and recursive policy-based concept induction in expressive logical fragments. I am particularly interested in bridging statistical learning with formal logic to improve robustness and interpretability in concept learning. Overall, my research aims to advance intelligent knowledge graph systems that are scalable, adaptive, and semantically grounded, enabling more effective integration, reasoning, and exploitation of large heterogeneous data sources.

Reviewing

My limited free time is partly consumed by tasks such as reviewing, for instance,”

• Journal of web semantics
• Semantic Web Journal
• GeoLD 2024