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, Istanbul . Previously, I worked as a research assistant, software engineer and consultant.

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

Recent Activities

November 2025 - I will be starting a research visit at Translational AI Lab - Amsterdam UMC for 3 months as of November, applying Neurosymbolic knowledge discovery to medical datasets.
October 2025 - On Sunday, October 26th, I will be presenting our work on Discovering Association Rules in High-Dimensional Small Tabular Data at the ECAI2025 conference workshop ANSyA. See our preprint on arXiv.
October 2025 - Aerial+ is now published in NeSy 2025 proceedings, Proceedings of Machine Learning Research (PMLR), volume 284.
September 2025 - Our new paper on "Learning Semantic Association Rules from Internet of Things Data" is now published in Neurosymbolic Artificial Intelligence Journal!
September 2025 - Our ECAI 2025 workshop paper "Discovering Association Rules in High-Dimensional Small Tabular Data" is now available on arXiv.
September 2025 - I wrote a trip report for the NeSy 2025 conference.
September 2025 - New software paper published in the SoftwareX journal, describing PyAerial: Scalable association rule mining from tabular data 🎉!
September 2025 - I attended the 19th Neurosymbolic Learning and Reasoning Conference (NeSy 2025) to present Aerial+ scalable association rule learner poster.
August 2025 - Our short paper entitled "Discovering Association Rules in High-Dimensional Small Tabular Data" got accepted at ECAI 2025 workshop on Advanced Neuro-Symbolic Applications 🎉.

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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). PDF
E. Karabulut, P. Groth, and V. Degeler. "Pyaerial: Scalable association rule mining from tabular data". SoftwareX, 31:102341, 2025. ISSN 2352-7110. PDF
E. Karabulut, P. Groth, V. Degeler, Neurosymbolic association rule mining from tabular data, in: Proceedings of The 19th International Conference on Neurosymbolic Learning and Reasoning, volume 284 of Proceedings of Machine Learning Research, PMLR, 2025, pp. 565–588. PDF
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. PDF

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

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). PDF

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