- E-Mail:
- cem.okulmus@uni-paderborn.de
- Phone:
- +49 5251 60-6627
- Web:
- Homepage
- Social Media:
- Office Address:
-
Fürstenallee 11
33102 Paderborn - Room:
- F2.108
About Cem Okulmus
I currently work as a PostDoc in the Knowledge Representation group at Paderborn University led by Anni-Yasmin Turhan. My recent work has focused on exploring learnability of constraints over graph-structured data in the context of RDF and SHACL.
Previously I was a postdoctoral fellow at Umeå University where I worked with (then) WASP guest professor Diego Calvanese on the topic of temporal ontology-based data-acess (OBDA).
I completed my PhD at TU Wien, where my thesis focussed on exploring the practical computation of structural decomposition methods for relational queries.
Topics I am interested in include
- Learnability/Seperability in various languages over graph-structured data
- Temporal Ontology-Based Data Access
- Ontology-Mediated Query Answering for Graph Queries
- Shape Constraint Langauges
- Structural Width Measures over Hypergraphs
Curriculum Vitae
Since 03.02.2025: Paderborn University
PostDoc, In the Group of Knowledge Representation group of Prof. Anni-Yasmin Turhan
16.01.2023 - 13.01.2025: Umeå University
WASP Postdoctoral fellow, In the Group of Diego Calvanese, (then) WASP guest professor, Umeå, Sweden
03.09.2018 - 28.11.2022: TU Wien
PhD in Computer Science, Supervised by Reinhard Pichler
05.10.2015 - 15.06.2018: TU Wien
Master Programme - Logic and Computation, Vienna, AT
07.10.2012 - 15.04.2015: University of Innsbruck
Bachelor of Computer Science, Innsbruck, AT
Publications
Latest Publications
Selective Use of Yannakakis’ Algorithm for Consistent Performance Gains
D. Böhm, G. Gottlob, M. Lanzinger, D.M. Longo, C. Okulmus, R. Pichler, A. Selzer, in: Proceedings of the 28th International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data (DOLAP 2026), Tampere, Finland, 2026.
Common Foundations for Recursive Shape Languages
C. Okulmus, S. Ahmetaj, I. Boneva, J. Hidders, M. Jakubowski, J.E. Labra Gayo, W. Martens, F. Mogavero, F. Murlak, Ognjen Savković, M. Šimkus, D. Tomaszuk, in: Proceedings of the 23rd International Conference on Principles of Knowledge Representation and Reasoning (KR 2026), 2026.
Common Foundations for SHACL, ShEx, and PG-Schema
S. Ahmetaj, I. Boneva, J. Hidders, K. Hose, M. Jakubowski, J.E. Labra Gayo, W. Martens, F. Mogavero, F. Murlak, C. Okulmus, A. Polleres, O. Savković, M. Šimkus, D. Tomaszuk, in: Proceedings of the ACM on Web Conference 2025, ACM, 2025, pp. 8–12.
Query Rewriting for Nested Navigational Queries over Property Graphs
B. Löhnert, N. Augsten, C. Okulmus, M. Ortiz, in: L. Tendera, Y. Ibanez Garcia, P. Koopmann (Eds.), Proceedings of the 38th International Workshop on Description Logics (DL 2025), Opole, Poland, September 3-6, 2025., Opole, Poland, 2025.
Soft and Constrained Hypertree Width
M. Lanzinger, C. Okulmus, R. Pichler, A. Selzer, G. Gottlob, Proceedings of the ACM on Management of Data 3 (2025) 1–25.
Show all publications
Teaching
Current Courses
- Proseminar: Perlen der theoretischen Informatik
Scientific Engagement
2026 | PC member of the 23rd International Conference on Principles of Knowledge Representation and Reasoning
PC member of the 23rd International Conference on Principles of Knowledge Representation and Reasoning
2025 | PC member of the 28th European Conference on Artificial Intelligence
PC member of the 28th European Conference on Artificial Intelligence
2025 | PC member of the 19th edition of the European Conference on Logics in Artificial Intelligence
PC member of the 19th edition of the European Conference on Logics in Artificial Intelligence
2024 | PC member of the 21st International Conference on Principles of Knowledge Representation and Reasoning
PC member of the 21st International Conference on Principles of Knowledge Representation and Reasoning
2023 | PC member of the 18th Edition of the European Conference on Logics in Artificial Intelligence
PC member of the 18th Edition of the European Conference on Logics in Artificial Intelligence