CV

Education

• Ph.D. in Computer Engineering, Ege University, 2018

Work Experience

• 2022 – present: Assistant Professor, Department of Advanced Computing Sciences (DACS), Maastricht University, Netherlands
• 2018 – 2022: Postdoctoral Researcher, Institute of Data Science, Maastricht University, Netherlands

Skills

• Knowledge Graphs & Semantic Web
  • OWL, RDF, SPARQL, ontology engineering
  • Linked Open Data, FAIR principles
• Neuro-symbolic AI
  • Vector embeddings & representation learning
  • Rule mining
  • LLM integration with knowledge graphs
• Machine Learning & Data Science
  • Deep learning, graph neural networks
  • Data integration and knowledge discovery
• Workflow systems & data curation

MSc Students Supervised

• Sara Falahatkar (2025) – Benchmarking The Reasoning Capabilities of Large Language Models: A Comprehensive Framework for Ontology-Based Evaluation
• Caroline Lange (2025) – Savoring Science: Exploiting Knowledge Graphs and NLP in the Quest for Improved Recipe Classification
• Ömer Durukan Kılıç (2025) – Similarity-Based Clustering for Root Cause Discovery in Clinical Knowledge Graphs
• Jonas Bei (2025) – Uncovering Research Data Management through Topic Modelling
• Timo Anthonijsz (2025) – Synthetic Data and Hybrid Embeddings for Entity Deduplication in Healthcare Organizational Knowledge Graphs
• Yonghui Guo (2024) – RecipeRAG: Generating Personalized Recipes with A Knowledge Graph-Based Retrieval-Augmented Generation System
• David Schimmel (2024) – Enhancing Ingredient Substitutions in Recipe Contexts by Augmenting Graph Neural Networks
• Arthur Haas (2023) – Keyword Suggestion for Paid Search Using an RDF Knowledge Graph

Publications

In-silico Prediction of Synergistic Anti-Cancer Drug Combinations Using Multi-omics Data

Celebi, R., Bear Don’t Walk, O., Movva, R. et al. In-silico Prediction of Synergistic Anti-Cancer Drug Combinations Using Multi-omics Data. Sci Rep 9, 8949 (2019). https://doi.org/10.1038/s41598-019-45236-6

Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings

Celebi, R., Uyar, H., Yasar, E. et al. Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings. BMC Bioinformatics 20, 726 (2019). https://doi.org/10.1186/s12859-019-3284-5

Towards FAIR protocols and workflows: the OpenPREDICT use case

Celebi, R., Moreira, J.R., Hassan, A.A., Ayyar, S., Ridder, L., Kuhn, T., & Dumontier, M. (2020). Towards FAIR protocols and workflows: the OpenPREDICT use case. PeerJ Computer Science, 6, e281.

User-friendly composition of FAIR workflows in a notebook environment

Richardson, R.A., Celebi, R., Van Der Burg, S., Smits, D., Ridder, L., Dumontier, M., & Kuhn, T. (2021). User-friendly composition of FAIR workflows in a notebook environment. Proceedings of the 11th Knowledge Capture Conference, 1-8.

Automated Identification of Food Substitutions Using Knowledge Graph Embeddings

Loesch, J., Meeckers, L., van Lier, I., de Boer, A., Dumontier, M., & Celebi, R. (2022). Automated Identification of Food Substitutions Using Knowledge Graph Embeddings. SWAT4HCLS, 19-28.

Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science

Unni, D.R., Moxon, S.A.T., Bada, M., Brush, M., Bruskiewich, R., Caufield, J.H., Celebi, R., et al. (2022). Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science. Clinical and Translational Science, 15(8), 1848-1855.

Progress toward a universal biomedical data translator

Fecho, K., Thessen, A.E., Baranzini, S.E., Bizon, C., Hadlock, J.J., Huang, S., Celebi, R., et al. (2022). Progress toward a universal biomedical data translator. Clinical and Translational Science, 15(8), 1838-1847.

Artificial intelligence based data curation: enabling a patient-centric European health data space

de Zegher, I., Norak, K., Steiger, D., Müller, H., Kalra, D., Scheenstra, B., Celebi, R., et al. (2024). Artificial intelligence based data curation: enabling a patient-centric European health data space. Frontiers in Medicine, 11, 1365501.

Talks

Data quality checks and provenance data in detecting problematic executions of AI tools used for data curation tasks

December 01, 2023

Invited Talk at i~HD Annual Conference 2023 (AI Track on Bias and Transparency), Belgium

AIDAVA: AI-Driven Approaches for Clinical Knowledge Graph Construction and Patient Data Integration

March 17, 2025

Invited Talk at International Conference, Ghent, Belgium

AIDAVA: Data Curation, EHDS Data Requests and Semantic Interoperability

March 01, 2026

Presentation at HDAB CoP AI Day (Health Data Access Body Community of Practice), Europe

Teaching

Building and Mining Knowledge Graphs

Semantic Web

Data Science and Analysis