Erkan Karabulut

Erkan Karabulut

PhD Student, University of Amsterdam

Biography

I am a doctoral researcher at INDElab, University of Amsterdam, advised by Dr. Victoria Degeler and Prof. Dr. Paul Groth.

My research focuses on interpretable decision-making through Neurosymbolic knowledge discovery and inference, applied to Digital Twins and tabular datasets.

I hold an MSc in Computer Science from TU Munich and a BSc in Computer Engineering from Yildiz Technical University, Istanbulİstanbul <3. Previously, I was a research assistant at fortiss and worked as a software engineer and consultant.

I am open to academic and non-academic collaborations -- feel free to reach out: e.karabulut@uva.nl

Recent Activities

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Publications

  • 2025 - E. Karabulut, D. Daza, P. Groth and V. Degeler. "Discovering Association Rules in High-Dimensional Small Tabular Data". In ANSyA’25: 1st International Workshop on Advanced Neuro-Symbolic Applications, co-located with 28th European Conference on Artificial Intelligence (ECAI 2025). Link.

  • 2025 - E. Karabulut, P. Groth, and V. Degeler. "Pyaerial: Scalable association rule mining from tabular data". SoftwareX, 31:102341, 2025. ISSN 2352-7110. Link.

  • 2025 - E. Karabulut, P. Groth, and V. Degeler. "Neurosymbolic association rule mining from tabular data". In Proceedings of the 19th Conference on Neurosymbolic Learning and Reasoning (NeSy 2025), Accepted/In Press. Link.

  • 2025 - Erkan Karabulut, Paul Groth, and Victoria Degeler. "Learning Semantic Association Rules from Internet of Things Data". Neurosymbolic Artificial Intelligence, 2025:1. doi:10.1177/29498732251377518. Link.

  • 2024 - Karabulut, Erkan, Paul Groth, and Victoria Degeler. "3K: Knowledge-Enriched Digital Twin Framework." (2024). In LongevIoT’24: 1st International Workshop on Longevity in IoT Systems, co-located with 14th International Conference on Internet of Things, November 19–22, 2024, Oulu, Finland. Link.

  • 2024 - Degeler, Victoria, et al. "DiTEC: Digital twin for evolutionary changes in water distribution networks." International Symposium on Leveraging Applications of Formal Methods. Cham: Springer Nature Switzerland, 2024. Link.

  • 2024 - Huang, Yiwen, Erkan Karabulut, and Victoria Degeler. "Large Language Model for Ontology Learning In Drinking Water Distribution Network Domain.". ELMKE’24: Evaluation of Language Models in Knowledge Engineering, co-located with 24th International Conference on Knowledge Engineering and Knowledge Management, 26-28 November, 2024, Amsterdam, The Netherlands. Link.

  • 2024 - Erkan Karabulut, Victoria Degeler, and Paul Groth. AE SemRL: Learning Semantic Association Rules with Autoencoders, 2024. (Earlier version of Aerial) Link.

  • 2023 - Karabulut, Erkan, Degeler, Victoria, and Groth, Paul. "Semantic Association Rule Learning from Time Series Data and Knowledge Graphs." In SemIIM’23: 2nd International Workshop on Semantic Industrial Information Modelling co-located with 22nd International Semantic Web Conference (ISWC 2023). Link.

Theses

  • MSc. Thesis: ML-based Data Classification and Data Aggregation on the Edge. May 2022. Supervisors: Prof. Dr. Rute C. Sofia, Prof. Dr.-Ing. Jörg Ott.

  • BSc. Thesis: Adaptive Learning-based Tracing Tool for Weather Research and Forecasting Software. January 2019. Supervisor: Assoc. Prof. Dr. Mehmet S. Aktaş
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